pyscf.scf package#
Submodules#
pyscf.scf.addons module#
 pyscf.scf.addons.convert_to_ghf(mf, out=None, remove_df=False)[source]#
Convert the given meanfield object to the generalized HF/KS object
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input mf object. If mf is an second order SCF (SOSCF) object, the SOSCF layer will be discarded. Its underlying SCF object mf._scf will be converted.
 Args:
mf : SCF object
 Kwargs
 remove_dfbool
Whether to convert the DFSCF object to the normal SCF object. This conversion is not applied by default.
 Returns:
An generalized SCF object
 pyscf.scf.addons.convert_to_rhf(mf, out=None, remove_df=False)[source]#
Convert the given meanfield object to the restricted HF/KS object
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input mf object. If mf is an second order SCF (SOSCF) object, the SOSCF layer will be discarded. Its underlying SCF object mf._scf will be converted.
 Args:
mf : SCF object
 Kwargs
 remove_dfbool
Whether to convert the DFSCF object to the normal SCF object. This conversion is not applied by default.
 Returns:
An unrestricted SCF object
 pyscf.scf.addons.convert_to_uhf(mf, out=None, remove_df=False)[source]#
Convert the given meanfield object to the unrestricted HF/KS object
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input mf object. If mf is an second order SCF (SOSCF) object, the SOSCF layer will be discarded. Its underlying SCF object mf._scf will be converted.
 Args:
mf : SCF object
 Kwargs
 remove_dfbool
Whether to convert the DFSCF object to the normal SCF object. This conversion is not applied by default.
 Returns:
An unrestricted SCF object
 pyscf.scf.addons.dynamic_level_shift(mf, factor=1.0)#
Dynamically change the level shift in each SCF cycle. The level shift value is set to (HF energy change * factor)
 pyscf.scf.addons.dynamic_level_shift_(mf, factor=1.0)[source]#
Dynamically change the level shift in each SCF cycle. The level shift value is set to (HF energy change * factor)
 pyscf.scf.addons.dynamic_occ(mf, tol=0.001)#
Dynamically adjust the occupancy to avoid degeneracy between HOMO and LUMO
 pyscf.scf.addons.dynamic_occ_(mf, tol=0.001)[source]#
Dynamically adjust the occupancy to avoid degeneracy between HOMO and LUMO
 pyscf.scf.addons.dynamic_sz_(mf)#
For UHF, allowing the Sz value being changed during SCF iteration. Determine occupation of alpha and beta electrons based on energy spectrum
 pyscf.scf.addons.fast_newton(mf, mo_coeff=None, mo_occ=None, dm0=None, auxbasis=None, dual_basis=None, **newton_kwargs)[source]#
This is a wrap function which combines several operations. This function first setup the initial guess from density fitting calculation then use for Newton solver and call Newton solver.
Newton solver attributes [max_cycle_inner, max_stepsize, ah_start_tol, ah_conv_tol, ah_grad_trust_region, …] can be passed through
**newton_kwargs
.
 pyscf.scf.addons.float_occ(mf)#
For UHF, allowing the Sz value being changed during SCF iteration. Determine occupation of alpha and beta electrons based on energy spectrum
 pyscf.scf.addons.float_occ_(mf)[source]#
For UHF, allowing the Sz value being changed during SCF iteration. Determine occupation of alpha and beta electrons based on energy spectrum
 pyscf.scf.addons.follow_state(mf, occorb=None)#
 pyscf.scf.addons.frac_occ(mf, tol=0.001)#
Addons for SCF methods to assign fractional occupancy for degenerated occupied HOMOs.
Examples:
>>> mf = gto.M(atom='O 0 0 0; O 0 0 1', verbose=4).RHF() >>> mf = scf.addons.frac_occ(mf) >>> mf.run()
 pyscf.scf.addons.frac_occ_(mf, tol=0.001)[source]#
Addons for SCF methods to assign fractional occupancy for degenerated occupied HOMOs.
Examples:
>>> mf = gto.M(atom='O 0 0 0; O 0 0 1', verbose=4).RHF() >>> mf = scf.addons.frac_occ(mf) >>> mf.run()
 pyscf.scf.addons.get_ghf_orbspin(mo_energy, mo_occ, is_rhf=None)[source]#
Spin of each GHF orbital when the GHF orbitals are converted from RHF/UHF orbitals
For RHF orbitals, the orbspin corresponds to first occupied orbitals then unoccupied orbitals. In the occupied orbital space, if degenerated, first alpha then beta, last the (openshell) singly occupied (alpha) orbitals. In the unoccupied orbital space, first the (openshell) unoccupied (beta) orbitals if applicable, then alpha and beta orbitals
For UHF orbitals, the orbspin corresponds to first occupied orbitals then unoccupied orbitals.
 pyscf.scf.addons.mom_occ(mf, occorb, setocc)#
Use maximum overlap method to determine occupation number for each orbital in every iteration.
 pyscf.scf.addons.mom_occ_(mf, occorb, setocc)[source]#
Use maximum overlap method to determine occupation number for each orbital in every iteration.
 pyscf.scf.addons.partial_cholesky_orth_(S, canthr=1e07, cholthr=1e09)[source]#
Partial Cholesky orthogonalization for curing overcompleteness.
References:
Susi Lehtola, Curing basis set overcompleteness with pivoted Cholesky decompositions, J. Chem. Phys. 151, 241102 (2019), doi:10.1063/1.5139948.
Susi Lehtola, Accurate reproduction of strongly repulsive interatomic potentials, Phys. Rev. A 101, 032504 (2020), doi:10.1103/PhysRevA.101.032504.
 pyscf.scf.addons.project_dm_nr2nr(mol1, dm1, mol2)[source]#
Project density matrix representation from basis set 1 (mol1) to basis set 2 (mol2).
\[ \begin{align}\begin{aligned}AO2\rangle DM_AO2 \langle AO2\\= AO2\rangle P DM_AO1 P \langle AO2\\DM_AO2 = P DM_AO1 P\\P = S_{AO2}^{1}\langle AO2AO1\rangle\end{aligned}\end{align} \]There are three relevant functions:
project_dm_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_dm_nr2r()
projects from nonrelativistic to relativistic basis.project_dm_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.project_dm_nr2r(mol1, dm1, mol2)[source]#
Project density matrix representation from basis set 1 (mol1) to basis set 2 (mol2).
\[ \begin{align}\begin{aligned}AO2\rangle DM_AO2 \langle AO2\\= AO2\rangle P DM_AO1 P \langle AO2\\DM_AO2 = P DM_AO1 P\\P = S_{AO2}^{1}\langle AO2AO1\rangle\end{aligned}\end{align} \]There are three relevant functions:
project_dm_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_dm_nr2r()
projects from nonrelativistic to relativistic basis.project_dm_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.project_dm_r2r(mol1, dm1, mol2)[source]#
Project density matrix representation from basis set 1 (mol1) to basis set 2 (mol2).
\[ \begin{align}\begin{aligned}AO2\rangle DM_AO2 \langle AO2\\= AO2\rangle P DM_AO1 P \langle AO2\\DM_AO2 = P DM_AO1 P\\P = S_{AO2}^{1}\langle AO2AO1\rangle\end{aligned}\end{align} \]There are three relevant functions:
project_dm_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_dm_nr2r()
projects from nonrelativistic to relativistic basis.project_dm_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.project_mo_nr2nr(mol1, mo1, mol2)[source]#
Project orbital coefficients from basis set 1 (C1 for mol1) to basis set 2 (C2 for mol2).
\[ \begin{align}\begin{aligned}\psi1\rangle = AO1\rangle C1\\\psi2\rangle = P \psi1\rangle = AO2\rangle S^{1}\langle AO2 AO1\rangle> C1 = AO2\rangle> C2\\C2 = S^{1}\langle AO2AO1\rangle C1\end{aligned}\end{align} \]There are three relevant functions:
project_mo_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_mo_nr2r()
projects from nonrelativistic to relativistic basis.project_mo_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.project_mo_nr2r(mol1, mo1, mol2)[source]#
Project orbital coefficients from basis set 1 (C1 for mol1) to basis set 2 (C2 for mol2).
\[ \begin{align}\begin{aligned}\psi1\rangle = AO1\rangle C1\\\psi2\rangle = P \psi1\rangle = AO2\rangle S^{1}\langle AO2 AO1\rangle> C1 = AO2\rangle> C2\\C2 = S^{1}\langle AO2AO1\rangle C1\end{aligned}\end{align} \]There are three relevant functions:
project_mo_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_mo_nr2r()
projects from nonrelativistic to relativistic basis.project_mo_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.project_mo_r2r(mol1, mo1, mol2)[source]#
Project orbital coefficients from basis set 1 (C1 for mol1) to basis set 2 (C2 for mol2).
\[ \begin{align}\begin{aligned}\psi1\rangle = AO1\rangle C1\\\psi2\rangle = P \psi1\rangle = AO2\rangle S^{1}\langle AO2 AO1\rangle> C1 = AO2\rangle> C2\\C2 = S^{1}\langle AO2AO1\rangle C1\end{aligned}\end{align} \]There are three relevant functions:
project_mo_nr2nr()
is the projection for nonrelativistic (scalar) basis.project_mo_nr2r()
projects from nonrelativistic to relativistic basis.project_mo_r2r()
is the projection between relativistic (spinor) basis.
 pyscf.scf.addons.remove_linear_dep(mf, threshold=1e08, lindep=1e10, cholesky_threshold=1e10, force_pivoted_cholesky=False)#
 Args:
 thresholdfloat
The threshold under which the eigenvalues of the overlap matrix are discarded to avoid numerical instability.
 lindepfloat
The threshold that triggers the special treatment of the linear dependence issue.
 pyscf.scf.addons.remove_linear_dep_(mf, threshold=1e08, lindep=1e10, cholesky_threshold=1e10, force_pivoted_cholesky=False)[source]#
 Args:
 thresholdfloat
The threshold under which the eigenvalues of the overlap matrix are discarded to avoid numerical instability.
 lindepfloat
The threshold that triggers the special treatment of the linear dependence issue.
pyscf.scf.atom_hf module#
 class pyscf.scf.atom_hf.AtomHF1e(mol)[source]#
Bases:
HF1e
,AtomSphAverageRHF
 eig(f, s)#
Solver for generalized eigenvalue problem
\[HC = SCE\]
 class pyscf.scf.atom_hf.AtomSphAverageRHF(mol)[source]#
Bases:
RHF
 check_sanity()[source]#
Check input of class/object attributes, check whether a class method is overwritten. It does not check the attributes which are prefixed with “_”. The return value of method set is the object itself. This allows a series of functions/methods to be executed in pipe.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 scf(*args, **kwargs)[source]#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 pyscf.scf.atom_hf.AtomSphericAverageRHF#
alias of
AtomSphAverageRHF
 pyscf.scf.atom_hf.frac_occ(symb, l, atomic_configuration=[[0, 0, 0, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 0, 0, 0], [4, 0, 0, 0], [4, 1, 0, 0], [4, 2, 0, 0], [4, 3, 0, 0], [4, 4, 0, 0], [4, 5, 0, 0], [4, 6, 0, 0], [5, 6, 0, 0], [6, 6, 0, 0], [6, 7, 0, 0], [6, 8, 0, 0], [6, 9, 0, 0], [6, 10, 0, 0], [6, 11, 0, 0], [6, 12, 0, 0], [7, 12, 0, 0], [8, 12, 0, 0], [8, 13, 0, 0], [8, 12, 2, 0], [8, 12, 3, 0], [8, 12, 4, 0], [6, 12, 7, 0], [6, 12, 8, 0], [6, 12, 9, 0], [6, 12, 10, 0], [7, 12, 10, 0], [8, 12, 10, 0], [8, 13, 10, 0], [8, 14, 10, 0], [8, 15, 10, 0], [8, 16, 10, 0], [8, 17, 10, 0], [8, 18, 10, 0], [9, 18, 10, 0], [10, 18, 10, 0], [10, 19, 10, 0], [10, 18, 12, 0], [10, 18, 13, 0], [8, 18, 16, 0], [8, 18, 17, 0], [8, 18, 18, 0], [8, 18, 19, 0], [8, 18, 20, 0], [9, 18, 20, 0], [10, 18, 20, 0], [10, 19, 20, 0], [10, 20, 20, 0], [10, 21, 20, 0], [10, 22, 20, 0], [10, 23, 20, 0], [10, 24, 20, 0], [11, 24, 20, 0], [12, 24, 20, 0], [12, 24, 21, 0], [12, 24, 22, 0], [12, 24, 21, 2], [12, 24, 20, 4], [12, 24, 20, 5], [12, 24, 20, 6], [12, 24, 20, 7], [11, 24, 20, 9], [10, 24, 20, 11], [10, 24, 20, 12], [10, 24, 20, 13], [10, 24, 20, 14], [11, 24, 20, 14], [12, 24, 20, 14], [12, 25, 20, 14], [12, 24, 22, 14], [12, 24, 23, 14], [10, 24, 26, 14], [10, 24, 27, 14], [10, 24, 28, 14], [10, 24, 29, 14], [10, 24, 30, 14], [11, 24, 30, 14], [12, 24, 30, 14], [12, 25, 30, 14], [12, 26, 30, 14], [12, 27, 30, 14], [12, 28, 30, 14], [12, 29, 30, 14], [12, 30, 30, 14], [13, 30, 30, 14], [14, 30, 30, 14], [14, 30, 31, 14], [14, 30, 32, 14], [14, 30, 30, 17], [14, 30, 30, 18], [14, 30, 30, 19], [13, 30, 30, 21], [12, 30, 30, 23], [12, 30, 30, 24], [12, 30, 30, 25], [12, 30, 30, 26], [12, 30, 30, 27], [12, 30, 30, 28], [13, 30, 30, 28], [14, 30, 30, 28], [14, 30, 31, 28], [14, 30, 32, 28], [14, 30, 33, 28], [12, 30, 36, 28], [12, 30, 37, 28], [12, 30, 38, 28], [12, 30, 39, 28], [12, 30, 40, 28], [13, 30, 40, 28], [14, 30, 40, 28], [14, 31, 40, 28], [14, 32, 40, 28], [14, 33, 40, 28], [14, 34, 40, 28], [14, 35, 40, 28], [14, 36, 40, 28]])[source]#
 pyscf.scf.atom_hf.get_atm_nrhf(mol, atomic_configuration=[[0, 0, 0, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 0, 0, 0], [4, 0, 0, 0], [4, 1, 0, 0], [4, 2, 0, 0], [4, 3, 0, 0], [4, 4, 0, 0], [4, 5, 0, 0], [4, 6, 0, 0], [5, 6, 0, 0], [6, 6, 0, 0], [6, 7, 0, 0], [6, 8, 0, 0], [6, 9, 0, 0], [6, 10, 0, 0], [6, 11, 0, 0], [6, 12, 0, 0], [7, 12, 0, 0], [8, 12, 0, 0], [8, 13, 0, 0], [8, 12, 2, 0], [8, 12, 3, 0], [8, 12, 4, 0], [6, 12, 7, 0], [6, 12, 8, 0], [6, 12, 9, 0], [6, 12, 10, 0], [7, 12, 10, 0], [8, 12, 10, 0], [8, 13, 10, 0], [8, 14, 10, 0], [8, 15, 10, 0], [8, 16, 10, 0], [8, 17, 10, 0], [8, 18, 10, 0], [9, 18, 10, 0], [10, 18, 10, 0], [10, 19, 10, 0], [10, 18, 12, 0], [10, 18, 13, 0], [8, 18, 16, 0], [8, 18, 17, 0], [8, 18, 18, 0], [8, 18, 19, 0], [8, 18, 20, 0], [9, 18, 20, 0], [10, 18, 20, 0], [10, 19, 20, 0], [10, 20, 20, 0], [10, 21, 20, 0], [10, 22, 20, 0], [10, 23, 20, 0], [10, 24, 20, 0], [11, 24, 20, 0], [12, 24, 20, 0], [12, 24, 21, 0], [12, 24, 22, 0], [12, 24, 21, 2], [12, 24, 20, 4], [12, 24, 20, 5], [12, 24, 20, 6], [12, 24, 20, 7], [11, 24, 20, 9], [10, 24, 20, 11], [10, 24, 20, 12], [10, 24, 20, 13], [10, 24, 20, 14], [11, 24, 20, 14], [12, 24, 20, 14], [12, 25, 20, 14], [12, 24, 22, 14], [12, 24, 23, 14], [10, 24, 26, 14], [10, 24, 27, 14], [10, 24, 28, 14], [10, 24, 29, 14], [10, 24, 30, 14], [11, 24, 30, 14], [12, 24, 30, 14], [12, 25, 30, 14], [12, 26, 30, 14], [12, 27, 30, 14], [12, 28, 30, 14], [12, 29, 30, 14], [12, 30, 30, 14], [13, 30, 30, 14], [14, 30, 30, 14], [14, 30, 31, 14], [14, 30, 32, 14], [14, 30, 30, 17], [14, 30, 30, 18], [14, 30, 30, 19], [13, 30, 30, 21], [12, 30, 30, 23], [12, 30, 30, 24], [12, 30, 30, 25], [12, 30, 30, 26], [12, 30, 30, 27], [12, 30, 30, 28], [13, 30, 30, 28], [14, 30, 30, 28], [14, 30, 31, 28], [14, 30, 32, 28], [14, 30, 33, 28], [12, 30, 36, 28], [12, 30, 37, 28], [12, 30, 38, 28], [12, 30, 39, 28], [12, 30, 40, 28], [13, 30, 40, 28], [14, 30, 40, 28], [14, 31, 40, 28], [14, 32, 40, 28], [14, 33, 40, 28], [14, 34, 40, 28], [14, 35, 40, 28], [14, 36, 40, 28]])[source]#
pyscf.scf.atom_hf_pp module#
 class pyscf.scf.atom_hf_pp.AtomHF1ePP(mol)[source]#

 eig(f, s)#
Solver for generalized eigenvalue problem
\[HC = SCE\]
 get_hcore(mol=None)#
 class pyscf.scf.atom_hf_pp.AtomSCFPP(mol)[source]#
Bases:
AtomSphAverageRHF
pyscf.scf.atom_ks module#
 class pyscf.scf.atom_ks.AtomSphAverageRKS(mol, *args, **kwargs)[source]#
Bases:
RKS
,AtomSphAverageRHF
 eig(f, s)#
Solver for generalized eigenvalue problem
\[HC = SCE\]
 get_grad(mo_coeff, mo_occ, fock=None)#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_occ(mo_energy=None, mo_coeff=None)#
spherically averaged fractional occupancy
 pyscf.scf.atom_ks.AtomSphericAverageRKS#
alias of
AtomSphAverageRKS
 pyscf.scf.atom_ks.get_atm_nrks(mol, atomic_configuration=[[0, 0, 0, 0], [1, 0, 0, 0], [2, 0, 0, 0], [3, 0, 0, 0], [4, 0, 0, 0], [4, 1, 0, 0], [4, 2, 0, 0], [4, 3, 0, 0], [4, 4, 0, 0], [4, 5, 0, 0], [4, 6, 0, 0], [5, 6, 0, 0], [6, 6, 0, 0], [6, 7, 0, 0], [6, 8, 0, 0], [6, 9, 0, 0], [6, 10, 0, 0], [6, 11, 0, 0], [6, 12, 0, 0], [7, 12, 0, 0], [8, 12, 0, 0], [8, 12, 1, 0], [8, 12, 2, 0], [8, 12, 3, 0], [8, 12, 4, 0], [7, 12, 6, 0], [7, 12, 7, 0], [7, 12, 8, 0], [7, 12, 9, 0], [7, 12, 10, 0], [8, 12, 10, 0], [8, 13, 10, 0], [8, 14, 10, 0], [8, 15, 10, 0], [8, 16, 10, 0], [8, 17, 10, 0], [8, 18, 10, 0], [9, 18, 10, 0], [10, 18, 10, 0], [10, 18, 11, 0], [10, 18, 12, 0], [10, 18, 13, 0], [9, 18, 15, 0], [9, 18, 16, 0], [8, 18, 18, 0], [8, 18, 19, 0], [8, 18, 20, 0], [9, 18, 20, 0], [10, 18, 20, 0], [10, 19, 20, 0], [10, 20, 20, 0], [10, 21, 20, 0], [10, 22, 20, 0], [10, 23, 20, 0], [10, 24, 20, 0], [11, 24, 20, 0], [12, 24, 20, 0], [12, 24, 20, 1], [12, 24, 20, 2], [12, 24, 20, 3], [12, 24, 20, 4], [12, 24, 20, 5], [12, 24, 20, 6], [12, 24, 20, 7], [12, 24, 20, 8], [12, 24, 20, 9], [12, 24, 20, 10], [12, 24, 20, 11], [12, 24, 20, 12], [12, 24, 20, 13], [12, 24, 20, 14], [12, 24, 21, 14], [12, 24, 22, 14], [12, 24, 23, 14], [11, 24, 25, 14], [11, 24, 26, 14], [10, 24, 28, 14], [10, 24, 29, 14], [10, 24, 30, 14], [11, 24, 30, 14], [12, 24, 30, 14], [12, 25, 30, 14], [12, 26, 30, 14], [12, 27, 30, 14], [12, 28, 30, 14], [12, 29, 30, 14], [12, 30, 30, 14], [13, 30, 30, 14], [14, 30, 30, 14], [14, 30, 30, 15], [14, 30, 30, 16], [14, 30, 30, 17], [13, 30, 30, 19], [13, 30, 30, 20], [13, 30, 30, 21], [13, 30, 30, 22], [13, 30, 30, 23], [13, 30, 30, 24], [13, 30, 30, 25], [13, 30, 30, 26], [13, 30, 30, 27], [13, 30, 30, 28], [14, 30, 30, 28], [14, 30, 31, 28], [14, 30, 32, 28], [13, 30, 34, 28], [12, 30, 36, 28], [12, 30, 37, 28], [12, 30, 38, 28], [12, 30, 39, 28], [12, 30, 40, 28], [13, 30, 40, 28], [14, 30, 40, 28], [14, 31, 40, 28], [14, 32, 40, 28], [14, 33, 40, 28], [14, 34, 40, 28], [14, 35, 40, 28], [14, 36, 40, 28]], xc='slater', grid=(100, 434))[source]#
pyscf.scf.chkfile module#
pyscf.scf.cphf module#
Restricted coupled perturbed HartreeFock solver
 pyscf.scf.cphf.kernel(fvind, mo_energy, mo_occ, h1, s1=None, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)#
 Args:
 fvindfunction
Given density matrix, compute (ijkl)D_{lk}*2  (ijkl)D_{jk}
 Kwargs:
 hermiboolean
Whether the matrix defined by fvind is Hermitian or not.
 level_shiftfloat
Add to diagonal terms to slightly improve the convergence speed of Krylov solver
 pyscf.scf.cphf.solve(fvind, mo_energy, mo_occ, h1, s1=None, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
 Args:
 fvindfunction
Given density matrix, compute (ijkl)D_{lk}*2  (ijkl)D_{jk}
 Kwargs:
 hermiboolean
Whether the matrix defined by fvind is Hermitian or not.
 level_shiftfloat
Add to diagonal terms to slightly improve the convergence speed of Krylov solver
 pyscf.scf.cphf.solve_nos1(fvind, mo_energy, mo_occ, h1, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
For field independent basis. First order overlap matrix is zero
 Kwargs:
 level_shiftfloat
Add to diagonal terms to slightly improve the convergence speed of Krylov solver
 pyscf.scf.cphf.solve_withs1(fvind, mo_energy, mo_occ, h1, s1, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
For field dependent basis. First order overlap matrix is nonzero. The first order orbitals are set to C^1_{ij} = 1/2 S1 e1 = h1  s1*e0 + (e0_je0_i)*c1 + vhf[c1]
 Kwargs:
 level_shiftfloat
Add to diagonal terms to slightly improve the convergence speed of Krylov solver
 Returns:
First order orbital coefficients (in MO basis) and first order orbital energy matrix
pyscf.scf.dhf module#
Dirac HartreeFock
 class pyscf.scf.dhf.DHF(mol)[source]#
Bases:
SCF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for DiracHartreeFock
 with_ssssbool or string, for DiracHartreeFock only
If False, ignore small component integrals (SSSS). Default is True.
 with_gauntbool, for DiracHartreeFock only
Default is False.
 with_breitbool, for DiracHartreeFock only
Gaunt + gauge term. Default is False.
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> e0 = mf.scf() >>> mf = scf.DHF(mol) >>> e1 = mf.scf() >>> print('Relativistic effects = %.12f' % (e1e0)) Relativistic effects = 0.000008854205
 Gradients(*args, **kwargs)#
Unrestricted DiracHartreeFock gradients
 TDA(*args, **kwargs)#
TammDancoff approximation
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e9.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 TDHF(*args, **kwargs)#
 analyze(verbose=None)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 conv_tol = 1e08#
 dip_moment(mol=None, dm=None, unit='Debye', verbose=3, **kwargs)[source]#
Dipole moment calculation
\[\begin{split}\mu_x = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nux\mu) + \sum_A Q_A X_A\\ \mu_y = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuy\mu) + \sum_A Q_A Y_A\\ \mu_z = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuz\mu) + \sum_A Q_A Z_A\end{split}\]where \(\mu_x, \mu_y, \mu_z\) are the x, y and z components of dipole moment
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices Return:
A list: the dipole moment on x, y and z component
 energy_elec(dm=None, h1e=None, vhf=None)#
Electronic part of DiracHartreeFock energy
 Args:
mf : an instance of SCF class
 Kwargs:
 dm2D ndarray
oneparticle density matrix
 h1e2D ndarray
Core hamiltonian
 vhf2D ndarray
HF potential
 Returns:
HartreeFock electronic energy and the Coulomb energy
 gen_response(mo_coeff=None, mo_occ=None, with_j=True, hermi=0, max_memory=None)#
Generate a function to compute the product of DHF response function and DHF density matrices.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_jk(mol=None, dm=None, hermi=1, with_j=True, with_k=True, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 get_occ(mo_energy=None, mo_coeff=None)[source]#
Label the occupancies for each orbital
 Kwargs:
 mo_energy1D ndarray
Obital energies
 mo_coeff2D ndarray
Obital coefficients
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 0, 2, 2, 2])
 init_guess_by_atom(mol=None)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 init_guess_by_chkfile(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 init_guess_by_mod_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_sap(mol=None, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 make_rdm1(mo_coeff=None, mo_occ=None, **kwargs)#
Oneparticle density matrix in AO representation
 Args:
 mo_coeff2D ndarray
Orbital coefficients. Each column is one orbital.
 mo_occ1D ndarray
Occupancy
 Returns:
Oneparticle density matrix, 2D ndarray
 mulliken_pop(mol=None, dm=None, s=None, verbose=5)[source]#
Mulliken population analysis
\[M_{ij} = D_{ij} S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\]
 scf(dm0=None)[source]#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 ssss_approx = 'Visscher'#
 stability(internal=None, external=None, verbose=None, return_status=False, **kwargs)[source]#
DHF/DKS stability analysis.
See also pyscf.scf.stability.rhf_stability function.
 Kwargs:
 return_status: bool
Whether to return stable_i and stable_e
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 to_dks(xc='HF')[source]#
Convert the input meanfield object to a DKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_ghf()[source]#
Convert the input meanfield object to a GHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_gks(xc=None)[source]#
Convert the input meanfield object to a GKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 to_ks(xc='HF')#
Convert the input meanfield object to a DKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_rhf()[source]#
Convert the input meanfield object to a RHF/ROHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_rks(xc=None)[source]#
Convert the input meanfield object to a RKS/ROKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_uhf()[source]#
Convert the input meanfield object to a UHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_uks(xc=None)[source]#
Convert the input meanfield object to a UKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 with_breit = False#
 with_gaunt = False#
 with_ssss = True#
 x2c()#
 class pyscf.scf.dhf.HF1e(mol)[source]#
Bases:
DHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 class pyscf.scf.dhf.RDHF(mol)[source]#
Bases:
DHF
Kramers restricted DiracHartreeFock
 TDA(*args, **kwargs)#
TammDancoff approximation
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e9.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 TDHF(*args, **kwargs)#
 to_dks(xc='HF')[source]#
Convert the input meanfield object to a DKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 x2c()#
 pyscf.scf.dhf.dip_moment(mol, dm, unit='Debye', verbose=3, **kwargs)[source]#
Dipole moment calculation
\[\begin{split}\mu_x = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nux\mu) + \sum_A Q_A X_A\\ \mu_y = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuy\mu) + \sum_A Q_A Y_A\\ \mu_z = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuz\mu) + \sum_A Q_A Z_A\end{split}\]where \(\mu_x, \mu_y, \mu_z\) are the x, y and z components of dipole moment
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices Return:
A list: the dipole moment on x, y and z component
 pyscf.scf.dhf.energy_elec(mf, dm=None, h1e=None, vhf=None)[source]#
Electronic part of DiracHartreeFock energy
 Args:
mf : an instance of SCF class
 Kwargs:
 dm2D ndarray
oneparticle density matrix
 h1e2D ndarray
Core hamiltonian
 vhf2D ndarray
HF potential
 Returns:
HartreeFock electronic energy and the Coulomb energy
 pyscf.scf.dhf.get_init_guess(mol, key='minao', **kwargs)[source]#
Generate density matrix for initial guess
 Kwargs:
 keystr
One of ‘minao’, ‘atom’, ‘huckel’, ‘mod_huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’.
 pyscf.scf.dhf.get_jk(mol, dm, hermi=1, coulomb_allow='SSSS', opt_llll=None, opt_ssll=None, opt_ssss=None, omega=None, verbose=None)#
 pyscf.scf.dhf.get_jk_coulomb(mol, dm, hermi=1, coulomb_allow='SSSS', opt_llll=None, opt_ssll=None, opt_ssss=None, omega=None, verbose=None)[source]#
 pyscf.scf.dhf.init_guess_by_chkfile(mol, chkfile_name, project=None)[source]#
Read SCF chkfile and make the density matrix for 4CDHF initial guess.
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 pyscf.scf.dhf.init_guess_by_huckel(mol)[source]#
Initial guess from onthefly Huckel, doi:10.1021/acs.jctc.8b01089.
 pyscf.scf.dhf.init_guess_by_minao(mol)[source]#
Initial guess in terms of the overlap to minimal basis.
 pyscf.scf.dhf.init_guess_by_mod_huckel(mol)[source]#
Initial guess from onthefly Huckel, doi:10.1021/acs.jctc.8b01089, employing the updated GWH rule from doi:10.1021/ja00480a005.
 pyscf.scf.dhf.init_guess_by_sap(mol, mf)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 pyscf.scf.dhf.kernel(mf, conv_tol=1e09, conv_tol_grad=None, dump_chk=True, dm0=None, callback=None, conv_check=True)[source]#
the modified SCF kernel for DiracHartreeFock. In this kernel, the SCF is carried out in three steps. First the 2electron part is approximated by large component integrals (LLLL); Next, (SSLL) the interaction between large and small components are added; Finally, converge the SCF with the small component contributions (SSSS)
pyscf.scf.diis module#
DIIS
 class pyscf.scf.diis.ADIIS(dev=None, filename=None, incore=False)[source]#
Bases:
DIIS
Ref: JCP 132, 054109 (2010); DOI:10.1063/1.3304922
 update(s, d, f, mf, h1e, vhf, *args, **kwargs)[source]#
Extrapolate vector
If xerr the error vector is given, this function will push the target
vector and error vector in the DIIS subspace, and use the error vector to extrapolate the vector and return the extrapolated vector. * If xerr is None, this function will take the difference between the current given vector and the last given vector as the error vector to extrapolate the vector.
 class pyscf.scf.diis.CDIIS(mf=None, filename=None, Corth=None)[source]#
Bases:
DIIS
 update(s, d, f, *args, **kwargs)[source]#
Extrapolate vector
If xerr the error vector is given, this function will push the target
vector and error vector in the DIIS subspace, and use the error vector to extrapolate the vector and return the extrapolated vector. * If xerr is None, this function will take the difference between the current given vector and the last given vector as the error vector to extrapolate the vector.
 class pyscf.scf.diis.EDIIS(dev=None, filename=None, incore=False)[source]#
Bases:
DIIS
SCFEDIIS Ref: JCP 116, 8255 (2002); DOI:10.1063/1.1470195
 update(s, d, f, mf, h1e, vhf, *args, **kwargs)[source]#
Extrapolate vector
If xerr the error vector is given, this function will push the target
vector and error vector in the DIIS subspace, and use the error vector to extrapolate the vector and return the extrapolated vector. * If xerr is None, this function will take the difference between the current given vector and the last given vector as the error vector to extrapolate the vector.
pyscf.scf.dispersion module#
dispersion correction for HF and DFT
pyscf.scf.ghf module#
Nonrelativistic generalized HartreeFock
 class pyscf.scf.ghf.GHF(mol)[source]#
Bases:
SCF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for GHF method
GHF orbital coefficients are 2D array. Let nao be the number of spatial AOs, mo_coeff[:nao] are the coefficients of AO with alpha spin; mo_coeff[nao:nao*2] are the coefficients of AO with beta spin.
 CISD(*args, **kwargs)#
 MP2(*args, **kwargs)#
 TDA(*args, **kwargs)#
TammDancoff approximation
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e9.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 TDHF(*args, **kwargs)#
 analyze(verbose=None, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 det_ovlp(mo1, mo2, occ1, occ2, ovlp=None)[source]#
Calculate the overlap between two different determinants. It is the product of single values of molecular orbital overlap matrix.
 Return:
 A list:
the product of single values: float x_a: \(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\) They are used to calculate asymmetric density matrix
 dip_moment(mol=None, dm=None, unit_symbol='Debye', origin=None, verbose=3)[source]#
Dipole moment calculation
\[\begin{split}\mu_x = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nux\mu) + \sum_A Q_A X_A\\ \mu_y = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuy\mu) + \sum_A Q_A Y_A\\ \mu_z = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuz\mu) + \sum_A Q_A Z_A\end{split}\]where \(\mu_x, \mu_y, \mu_z\) are the x, y and z components of dipole moment
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices origin : optional; length 3 list, tuple, or 1D arrayLocation of the origin. By default, the point (0, 0, 0) is used.
 Return:
A list: the dipole moment on x, y and z component
 gen_response(mo_coeff=None, mo_occ=None, with_j=True, hermi=0, max_memory=None)#
Generate a function to compute the product of GHF response function and GHF density matrices.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_init_guess(mol=None, key='minao', **kwargs)#
 get_jk(mol=None, dm=None, hermi=0, with_j=True, with_k=True, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 get_occ(mo_energy=None, mo_coeff=None)#
Label the occupancies for each orbital
 Kwargs:
 mo_energy1D ndarray
Obital energies
 mo_coeff2D ndarray
Obital coefficients
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 0, 2, 2, 2])
 get_veff(mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1)[source]#
HartreeFock potential matrix for the given density matrix
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
matrix Vhf = 2*J  K. Vhf can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dm0 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf0 = scf.hf.get_veff(mol, dm0, hermi=0) >>> dm1 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf1 = scf.hf.get_veff(mol, dm1, hermi=0) >>> vhf2 = scf.hf.get_veff(mol, dm1, dm_last=dm0, vhf_last=vhf0, hermi=0) >>> numpy.allclose(vhf1, vhf2) True
 init_guess_by_atom(mol=None)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 init_guess_by_chkfile(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 init_guess_by_minao(mol=None)[source]#
Generate initial guess density matrix based on ANO basis, then project the density matrix to the basis set defined by
mol
 Returns:
Density matrix, 2D ndarray
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> scf.hf.init_guess_by_minao(mol) array([[ 0.94758917, 0.09227308], [ 0.09227308, 0.94758917]])
 init_guess_by_mod_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_sap(mol=None, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 mulliken_meta(mol=None, dm=None, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 mulliken_pop(mol=None, dm=None, s=None, verbose=5)[source]#
Mulliken population analysis
\[M_{ij} = D_{ij} S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\] Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 spin_square(mo_coeff=None, s=None)[source]#
Spin of the GHF wavefunction
\[S^2 = \frac{1}{2}(S_+ S_ + S_ S_+) + S_z^2\]where \(S_+ = \sum_i S_{i+}\) is effective for all beta occupied orbitals; \(S_ = \sum_i S_{i}\) is effective for all alpha occupied orbitals.
 There are two possibilities for \(S_+ S_\)
same electron \(S_+ S_ = \sum_i s_{i+} s_{i}\),
\[\sum_i \langle UHFs_{i+} s_{i}UHF\rangle = \sum_{pq}\langle ps_+s_q\rangle \gamma_{qp} = n_\alpha\]2) different electrons \(S_+ S_ = \sum s_{i+} s_{j}, (i\neq j)\). There are in total \(n(n1)\) terms. As a twoparticle operator,
\[\langle S_+ S_ \rangle =\sum_{ij}(\langle i^\alphai^\beta\rangle \langle j^\betaj^\alpha\rangle  \langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle)\]
 Similarly, for \(S_ S_+\)
same electron
\[\sum_i \langle s_{i} s_{i+}\rangle = n_\beta\]different electrons
\[\langle S_ S_+ \rangle =\sum_{ij}(\langle i^\betai^\alpha\rangle \langle j^\alphaj^\beta\rangle  \langle i^\betaj^\alpha\rangle \langle j^\alphai^\beta\rangle)\]
 For \(S_z^2\)
same electron
\[\langle s_z^2\rangle = \frac{1}{4}(n_\alpha + n_\beta)\]different electrons
\[\begin{split}&\sum_{ij}(\langle ijs_{z1}s_{z2}ij\rangle \langle ijs_{z1}s_{z2}ji\rangle) \\ &=\frac{1}{4}\sum_{ij}(\langle i^\alphai^\alpha\rangle \langle j^\alphaj^\alpha\rangle  \langle i^\alphai^\alpha\rangle \langle j^\betaj^\beta\rangle  \langle i^\betai^\beta\rangle \langle j^\alphaj^\alpha\rangle + \langle i^\betai^\beta\rangle \langle j^\betaj^\beta\rangle) \\ &\frac{1}{4}\sum_{ij}(\langle i^\alphaj^\alpha\rangle \langle j^\alphai^\alpha\rangle  \langle i^\alphaj^\alpha\rangle \langle j^\betai^\beta\rangle  \langle i^\betaj^\beta\rangle \langle j^\alphai^\alpha\rangle + \langle i^\betaj^\beta\rangle\langle j^\betai^\beta\rangle) \\ &=\frac{1}{4}\sum_{ij}\langle i^\alphai^\alpha\rangle  \langle i^\betai^\beta\rangle^2 \frac{1}{4}\sum_{ij}\langle i^\alphaj^\alpha\rangle  \langle i^\betaj^\beta\rangle^2 \\ &=\frac{1}{4}(n_\alpha  n_\beta)^2 \frac{1}{4}\sum_{ij}\langle i^\alphaj^\alpha\rangle  \langle i^\betaj^\beta\rangle^2\end{split}\]
 Args:
 moa list of 2 ndarrays
Occupied alpha and occupied beta orbitals
 Kwargs:
 sndarray
AO overlap
 Returns:
A list of two floats. The first is the expectation value of S^2. The second is the corresponding 2S+1
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> mo = (mf.mo_coeff[0][:,mf.mo_occ[0]>0], mf.mo_coeff[1][:,mf.mo_occ[1]>0]) >>> print('S^2 = %.7f, 2S+1 = %.7f' % spin_square(mo, mol.intor('int1e_ovlp_sph'))) S^2 = 0.7570150, 2S+1 = 2.0070027
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 with_soc = None#
 x2c()#
X2C with spinorbit coupling effects.
Starting from PySCF 2.1, this function (mol.GHF().x2c()) produces an X2C calculation in spherical GTO bases. The results in theory are equivalent to those obtained from the mol.X2C() method, which is computed in the spinor GTO bases. This function called the spinfree X2C1E method in the older versions.
Please note the difference from other SCF methods, such as RHF and UHF. In those methods, the .x2c() method produces a scalar relativistic calculation using the X2C Hamiltonian.
 x2c1e()[source]#
X2C with spinorbit coupling effects.
Starting from PySCF 2.1, this function (mol.GHF().x2c()) produces an X2C calculation in spherical GTO bases. The results in theory are equivalent to those obtained from the mol.X2C() method, which is computed in the spinor GTO bases. This function called the spinfree X2C1E method in the older versions.
Please note the difference from other SCF methods, such as RHF and UHF. In those methods, the .x2c() method produces a scalar relativistic calculation using the X2C Hamiltonian.
 class pyscf.scf.ghf.HF1e(mol)[source]#
Bases:
GHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 pyscf.scf.ghf.analyze(mf, verbose=5, with_meta_lowdin=True, origin=None, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Dipole moment
 pyscf.scf.ghf.det_ovlp(mo1, mo2, occ1, occ2, ovlp)[source]#
Calculate the overlap between two different determinants. It is the product of single values of molecular orbital overlap matrix.
 Return:
 A list:
the product of single values: float x_a: \(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\) They are used to calculate asymmetric density matrix
 pyscf.scf.ghf.get_jk(mol, dm, hermi=0, with_j=True, with_k=True, jkbuild=<function get_jk>, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 pyscf.scf.ghf.guess_orbspin(mo_coeff)[source]#
Guess the orbital spin (alpha 0, beta 1, unknown 1) based on the orbital coefficients
 pyscf.scf.ghf.init_guess_by_chkfile(mol, chkfile_name, project=None)[source]#
Read SCF chkfile and make the density matrix for GHF initial guess.
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 pyscf.scf.ghf.mulliken_meta(mol, dm_ao, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs.
 pyscf.scf.ghf.spin_square(mo, s=1)[source]#
Spin of the GHF wavefunction
\[S^2 = \frac{1}{2}(S_+ S_ + S_ S_+) + S_z^2\]where \(S_+ = \sum_i S_{i+}\) is effective for all beta occupied orbitals; \(S_ = \sum_i S_{i}\) is effective for all alpha occupied orbitals.
 There are two possibilities for \(S_+ S_\)
same electron \(S_+ S_ = \sum_i s_{i+} s_{i}\),
\[\sum_i \langle UHFs_{i+} s_{i}UHF\rangle = \sum_{pq}\langle ps_+s_q\rangle \gamma_{qp} = n_\alpha\]2) different electrons \(S_+ S_ = \sum s_{i+} s_{j}, (i\neq j)\). There are in total \(n(n1)\) terms. As a twoparticle operator,
\[\langle S_+ S_ \rangle =\sum_{ij}(\langle i^\alphai^\beta\rangle \langle j^\betaj^\alpha\rangle  \langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle)\]
 Similarly, for \(S_ S_+\)
same electron
\[\sum_i \langle s_{i} s_{i+}\rangle = n_\beta\]different electrons
\[\langle S_ S_+ \rangle =\sum_{ij}(\langle i^\betai^\alpha\rangle \langle j^\alphaj^\beta\rangle  \langle i^\betaj^\alpha\rangle \langle j^\alphai^\beta\rangle)\]
 For \(S_z^2\)
same electron
\[\langle s_z^2\rangle = \frac{1}{4}(n_\alpha + n_\beta)\]different electrons
\[\begin{split}&\sum_{ij}(\langle ijs_{z1}s_{z2}ij\rangle \langle ijs_{z1}s_{z2}ji\rangle) \\ &=\frac{1}{4}\sum_{ij}(\langle i^\alphai^\alpha\rangle \langle j^\alphaj^\alpha\rangle  \langle i^\alphai^\alpha\rangle \langle j^\betaj^\beta\rangle  \langle i^\betai^\beta\rangle \langle j^\alphaj^\alpha\rangle + \langle i^\betai^\beta\rangle \langle j^\betaj^\beta\rangle) \\ &\frac{1}{4}\sum_{ij}(\langle i^\alphaj^\alpha\rangle \langle j^\alphai^\alpha\rangle  \langle i^\alphaj^\alpha\rangle \langle j^\betai^\beta\rangle  \langle i^\betaj^\beta\rangle \langle j^\alphai^\alpha\rangle + \langle i^\betaj^\beta\rangle\langle j^\betai^\beta\rangle) \\ &=\frac{1}{4}\sum_{ij}\langle i^\alphai^\alpha\rangle  \langle i^\betai^\beta\rangle^2 \frac{1}{4}\sum_{ij}\langle i^\alphaj^\alpha\rangle  \langle i^\betaj^\beta\rangle^2 \\ &=\frac{1}{4}(n_\alpha  n_\beta)^2 \frac{1}{4}\sum_{ij}\langle i^\alphaj^\alpha\rangle  \langle i^\betaj^\beta\rangle^2\end{split}\]
 Args:
 moa list of 2 ndarrays
Occupied alpha and occupied beta orbitals
 Kwargs:
 sndarray
AO overlap
 Returns:
A list of two floats. The first is the expectation value of S^2. The second is the corresponding 2S+1
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> mo = (mf.mo_coeff[0][:,mf.mo_occ[0]>0], mf.mo_coeff[1][:,mf.mo_occ[1]>0]) >>> print('S^2 = %.7f, 2S+1 = %.7f' % spin_square(mo, mol.intor('int1e_ovlp_sph'))) S^2 = 0.7570150, 2S+1 = 2.0070027
pyscf.scf.ghf_symm module#
Nonrelativistic generalized HartreeFock with point group symmetry.
 pyscf.scf.ghf_symm.GHF#
alias of
SymAdaptedGHF
 class pyscf.scf.ghf_symm.HF1e(mol)[source]#
Bases:
SymAdaptedGHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 class pyscf.scf.ghf_symm.SymAdaptedGHF(mol)[source]#
Bases:
GHF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for GHF method
GHF orbital coefficients are 2D array. Let nao be the number of spatial AOs, mo_coeff[:nao] are the coefficients of AO with alpha spin; mo_coeff[nao:nao*2] are the coefficients of AO with beta spin.
 Attributes for symmetry allowed GHF:
 irrep_nelecdict
Specify the number of electrons for particular irrep {‘ir_name’:int, …}. For the irreps not listed in these dicts, the program will choose the occupancy based on the orbital energies.
 analyze(verbose=None, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 canonicalize(mo_coeff, mo_occ, fock=None)#
Canonicalization diagonalizes the UHF Fock matrix in occupied, virtual subspaces separatedly (without change occupancy).
 eig(h, s, symm_orb=None, irrep_id=None)[source]#
Solver for generalized eigenvalue problem
\[HC = SCE\]
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_irrep_nelec(mol=None, mo_coeff=None, mo_occ=None, s=None)[source]#
Electron numbers for each irreducible representation.
 Args:
 molan instance of
Mole
To provide irrep_id, and spinadapted basis
 mo_coeff2D ndarray
Regular orbital coefficients, without grouping for irreps
 mo_occ1D ndarray
Regular occupancy, without grouping for irreps
 molan instance of
 Returns:
 irrep_nelecdict
The number of electrons for each irrep {‘ir_name’:int,…}.
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', symmetry=True, verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() 76.016789472074251 >>> scf.hf_symm.get_irrep_nelec(mol, mf.mo_coeff, mf.mo_occ) {'A1': 6, 'A2': 0, 'B1': 2, 'B2': 2}
 get_occ(mo_energy=None, mo_coeff=None)[source]#
We assumed mo_energy are grouped by symmetry irreps, (see function self.eig). The orbitals are sorted after SCF.
 property orbsym#
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
pyscf.scf.hf module#
HartreeFock
 pyscf.scf.hf.Kgwh(Ei, Ej, updated_rule=False)[source]#
Computes the generalized WolfsbergHelmholtz parameter
 class pyscf.scf.hf.RHF(mol)[source]#
Bases:
SCF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 CASCI(*args, **kwargs)#
 CASSCF(*args, **kwargs)#
CASCI/CASSCF
 Args:
 mf_or_molSCF object or Mole object
SCF or Mole to define the problem size.
 ncasint
Number of active orbitals.
 nelecasint or a pair of int
Number of electrons in active space.
 Kwargs:
 ncoreint
Number of doubly occupied core orbitals. If not presented, this parameter can be automatically determined.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
. max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
. ncasint
Active space size.
 nelecastuple of int
Active (nelec_alpha, nelec_beta)
 ncoreint or tuple of int
Core electron number. In UHFCASSCF, it’s a tuple to indicate the different core electron numbers.
 natorbbool
Whether to transform natural orbitals in active space. Note: when CASCI/CASSCF are combined with DMRG solver or selected CI solver, enabling this parameter may slightly change the total energy. False by default.
 canonicalizationbool
Whether to canonicalize orbitals in core and external space against the general Fock matrix. The orbitals in active space are NOT transformed by default. To get the natural orbitals in active space, the attribute .natorb needs to be enabled. True by default.
 sorting_mo_energybool
Whether to sort the orbitals based on the diagonal elements of the general Fock matrix. Default is False.
 fcisolveran instance of
FCISolver
The pyscf.fci module provides several FCISolver for different scenario. Generally, fci.direct_spin1.FCISolver can be used for all RHFCASSCF. However, a proper FCISolver can provide better performance and better numerical stability. One can either use
fci.solver()
function to pick the FCISolver by the program or manually assigen the FCISolver to this attribute, e.g.>>> from pyscf import fci >>> mc = mcscf.CASSCF(mf, 4, 4) >>> mc.fcisolver = fci.solver(mol, singlet=True) >>> mc.fcisolver = fci.direct_spin1.FCISolver(mol)
You can control FCISolver by setting e.g.:
>>> mc.fcisolver.max_cycle = 30 >>> mc.fcisolver.conv_tol = 1e7
For more details of the parameter for FCISolver, See
fci
.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
When canonicalization is specified, the orbitals are canonical orbitals which make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is specified, the active segment of the mo_coeff is natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
 mo_occndarray
Occupation numbers of natural orbitals if natorb is specified.
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASCI(mf, 6, 6) >>> mc.kernel()[0] 108.980200816243354
Extra attributes for CASSCF:
 conv_tolfloat
Converge threshold. Default is 1e7
 conv_tol_gradfloat
Converge threshold for CI gradients and orbital rotation gradients. If not specified, it is set to sqrt(conv_tol).
 max_stepsizefloat
The step size for orbital rotation. Small step (0.005  0.05) is prefered. Default is 0.02.
 max_cycle_macroint
Max number of macro iterations. Default is 50.
 max_cycle_microint
Max number of micro iterations in each macro iteration. Depending on systems, increasing this value might reduce the total macro iterations. Generally, 2  5 steps should be enough. Default is 4.
 small_rot_tolfloat
Threshold for orbital rotation to be considered small. If the largest orbital rotation is smaller than this value, the CI solver will restart from the previous iteration if supported. Default is 0.01
 ah_level_shiftfloat, for AH solver.
Level shift for the Davidson diagonalization in AH solver. Default is 1e8.
 ah_conv_tolfloat, for AH solver.
converge threshold for AH solver. Default is 1e12.
 ah_max_cyclefloat, for AH solver.
Max number of iterations allowd in AH solver. Default is 30.
 ah_lindepfloat, for AH solver.
Linear dependence threshold for AH solver. Default is 1e14.
 ah_start_tolflat, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 2.5.
 ah_start_cycleint, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 3.
ah_conv_tol
,ah_max_cycle
,ah_lindep
,ah_start_tol
andah_start_cycle
can affect the accuracy and performance of CASSCF solver. Lowerah_conv_tol
andah_lindep
might improve the accuracy of CASSCF optimization, but decrease the performance.>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.UHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.conv_tol = 1e10 >>> mc.ah_conv_tol = 1e5 >>> mc.kernel()[0] 109.044401898486001 >>> mc.ah_conv_tol = 1e10 >>> mc.kernel()[0] 109.044401887945668
 chkfilestr
Checkpoint file to save the intermediate orbitals during the CASSCF optimization. Default is the checkpoint file of mean field object.
 ci_response_spaceint
subspace size to solve the CI vector response. Default is 3.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. scale_restorationfloat
When a step of orbital rotation moves out of trust region, the orbital optimization will be restored to previous state and the step size of the orbital rotation needs to be reduced. scale_restoration controls how much to scale down the step size.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
Optimized CASSCF orbitals coefficients. When canonicalization is specified, the returned orbitals make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is enabled, the active segment of mo_coeff is transformed to natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.kernel()[0] 109.044401882238134
 CISD(*args, **kwargs)#
 DFMP2(*args, **kwargs)#
 Gradients(*args, **kwargs)#
Nonrelativistic restricted HartreeFock gradients
 MP2(*args, **kwargs)#
restricted MP2 with canonical HF and noncanonical HF reference
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
 conv_tolfloat
For noncanonical MP2, converge threshold for MP2 correlation energy. Default value is 1e7.
 conv_tol_normtfloat
For noncanonical MP2, converge threshold for norm(t2). Default value is 1e5.
 max_cycleint
For noncanonical MP2, max number of MP2 iterations. Default value is 50.
 diis_spaceint
For noncanonical MP2, DIIS space size in MP2 iterations. Default is 6.
 level_shiftfloat
A shift on virtual orbital energies to stabilize the MP2 iterations.
 frozenint or list
If integer is given, the innermost orbitals are excluded from MP2 amplitudes. Given the orbital indices (0based) in a list, both occupied and virtual orbitals can be frozen in MP2 calculation.
>>> mol = gto.M(atom = 'H 0 0 0; F 0 0 1.1', basis = 'ccpvdz') >>> mf = scf.RHF(mol).run() >>> # freeze 2 core orbitals >>> pt = mp.MP2(mf).set(frozen = 2).run() >>> # autogenerate the number of core orbitals to be frozen (1 in this case) >>> pt = mp.MP2(mf).set_frozen().run() >>> # freeze 2 core orbitals and 3 high lying unoccupied orbitals >>> pt.set(frozen = [0,1,16,17,18]).run()
Saved results
 e_corrfloat
MP2 correlation correction
 e_corr_ss/osfloat
Samespin and oppositespin component of the MP2 correlation energy
 e_totfloat
Total MP2 energy (HF + correlation)
 t2 :
T amplitudes t2[i,j,a,b] (i,j in occ, a,b in virt)
 TDA(*args, **kwargs)#
TammDancoff approximation
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e9.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 TDHF(*args, **kwargs)#
Timedependent HartreeFock
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e4.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 check_sanity()[source]#
Check input of class/object attributes, check whether a class method is overwritten. It does not check the attributes which are prefixed with “_”. The return value of method set is the object itself. This allows a series of functions/methods to be executed in pipe.
 gen_response(mo_coeff=None, mo_occ=None, singlet=None, hermi=0, max_memory=None)#
Generate a function to compute the product of RHF response function and RHF density matrices.
 Kwargs:
 singlet (None or boolean)If singlet is None, response function for
orbital hessian or CPHF will be generated. If singlet is boolean, it is used in TDDFT response kernel.
 get_jk(mol=None, dm=None, hermi=1, with_j=True, with_k=True, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 get_veff(mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1)[source]#
HartreeFock potential matrix for the given density matrix
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
matrix Vhf = 2*J  K. Vhf can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dm0 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf0 = scf.hf.get_veff(mol, dm0, hermi=0) >>> dm1 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf1 = scf.hf.get_veff(mol, dm1, hermi=0) >>> vhf2 = scf.hf.get_veff(mol, dm1, dm_last=dm0, vhf_last=vhf0, hermi=0) >>> numpy.allclose(vhf1, vhf2) True
 stability(internal=True, external=False, verbose=None, return_status=False, **kwargs)[source]#
RHF/RKS stability analysis.
See also pyscf.scf.stability.rhf_stability function.
 Kwargs:
 internalbool
Internal stability, within the RHF optimization space.
 externalbool
External stability. Including the RHF > UHF and real > complex stability analysis.
 return_status: bool
Whether to return stable_i and stable_e
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 class pyscf.scf.hf.SCF(mol)[source]#
Bases:
StreamObject
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 CCSD(frozen=None, mo_coeff=None, mo_occ=None)#
restricted CCSD
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
 conv_tolfloat
converge threshold. Default is 1e7.
 conv_tol_normtfloat
converge threshold for norm(t1,t2). Default is 1e5.
 max_cycleint
max number of iterations. Default is 50.
 diis_spaceint
DIIS space size. Default is 6.
 diis_start_cycleint
The step to start DIIS. Default is 0.
 iterative_dampingfloat
The self consistent damping parameter.
 directbool
AOdirect CCSD. Default is False.
 async_iobool
Allow for asynchronous function execution. Default is True.
 incore_completebool
Avoid all I/O (also for DIIS). Default is False.
 level_shiftfloat
A shift on virtual orbital energies to stabilize the CCSD iteration
 frozenint or list
If integer is given, the innermost orbitals are frozen from CC amplitudes. Given the orbital indices (0based) in a list, both occupied and virtual orbitals can be frozen in CC calculation.
>>> mol = gto.M(atom = 'H 0 0 0; F 0 0 1.1', basis = 'ccpvdz') >>> mf = scf.RHF(mol).run() >>> # freeze 2 core orbitals >>> mycc = cc.CCSD(mf).set(frozen = 2).run() >>> # autogenerate the number of core orbitals to be frozen (1 in this case) >>> mycc = cc.CCSD(mf).set_frozen().run() >>> # freeze 2 core orbitals and 3 high lying unoccupied orbitals >>> mycc.set(frozen = [0,1,16,17,18]).run()
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment.
Saved results:
 convergedbool
Whether the CCSD iteration converged
 e_corrfloat
CCSD correlation correction
 e_totfloat
Total CCSD energy (HF + correlation)
 t1, t2 :
T amplitudes t1[i,a], t2[i,j,a,b] (i,j in occ, a,b in virt)
 l1, l2 :
Lambda amplitudes l1[i,a], l2[i,j,a,b] (i,j in occ, a,b in virt)
 cyclesint
The number of iteration cycles performed
 QCISD(frozen=None, mo_coeff=None, mo_occ=None)#
restricted QCISD
 QMMM(atoms_or_coords, charges, radii=None, unit=None)#
Embedding the oneelectron (nonrelativistic) potential generated by MM point charges into QM Hamiltonian.
The total energy includes the regular QM energy, the interaction between the nuclei in QM region and the MM charges, and the static Coulomb interaction between the electron density and the MM charges. It does not include the static Coulomb interactions of the MM point charges, the MM energy, the vdw interaction or other bonding/nonbonding effects between QM region and MM particles.
 Args:
scf_method : a HF or DFT object
 atoms_or_coords2D array, shape (N,3)
MM particle coordinates
 charges1D array
MM particle charges
 Kwargs:
 radii1D array
The Gaussian charge distribution radii of MM atoms.
 unitstr
Bohr, AU, Ang (case insensitive). Default is the same to mol.unit
 Returns:
Same method object as the input scf_method with modified 1e Hamiltonian
 Note:
1. if MM charge and X2C correction are used together, function mm_charge needs to be applied after X2C decoration (.x2c method), eg mf = mm_charge(scf.RHF(mol).x2c()), [(0.5,0.6,0.8)], [0.5]). 2. Once mm_charge function is applied on the SCF object, it affects all the postHF calculations eg MP2, CCSD, MCSCF etc
Examples:
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1', basis='ccpvdz', verbose=0) >>> mf = mm_charge(dft.RKS(mol), [(0.5,0.6,0.8)], [0.3]) >>> mf.kernel() 101.940495711284
 analyze(verbose=None, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 apply(fn, *args, **kwargs)[source]#
Apply the fn to rest arguments: return fn(*args, **kwargs). The return value of method set is the object itself. This allows a series of functions/methods to be executed in pipe.
 as_scanner()#
Generating a scanner/solver for HF PES.
The returned solver is a function. This function requires one argument “mol” as input and returns total HF energy.
The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the SCF object (DIIS, conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, …) during calculation.
Examples:
>>> from pyscf import gto, scf >>> hf_scanner = scf.RHF(gto.Mole().set(verbose=0)).as_scanner() >>> hf_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1')) 98.552190448277955 >>> hf_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5')) 98.414750424294368
 callback = None#
 canonicalize(mo_coeff, mo_occ, fock=None)#
Canonicalization diagonalizes the Fock matrix within occupied, open, virtual subspaces separatedly (without change occupancy).
 check_convergence = None#
 check_sanity()[source]#
Check input of class/object attributes, check whether a class method is overwritten. It does not check the attributes which are prefixed with “_”. The return value of method set is the object itself. This allows a series of functions/methods to be executed in pipe.
 conv_check = True#
 conv_tol = 1e09#
 conv_tol_cpscf = 1e08#
 conv_tol_grad = None#
 damp = 0#
 diis = True#
 diis_damp = 0#
 diis_file = None#
 diis_space = 8#
 diis_space_rollback = 0#
 diis_start_cycle = 1#
 dip_moment(mol=None, dm=None, unit='Debye', origin=None, verbose=3, **kwargs)[source]#
Dipole moment calculation
\[\begin{split}\mu_x = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nux\mu) + \sum_A Q_A X_A\\ \mu_y = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuy\mu) + \sum_A Q_A Y_A\\ \mu_z = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuz\mu) + \sum_A Q_A Z_A\end{split}\]where \(\mu_x, \mu_y, \mu_z\) are the x, y and z components of dipole moment
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices origin : optional; length 3 list, tuple, or 1D arrayLocation of the origin. By default, the point (0, 0, 0) is used.
 Return:
A list: the dipole moment on x, y and z component
 direct_scf = True#
 direct_scf_tol = 1e13#
 disp = None#
 do_disp(disp=None)#
Check whether to apply dispersion correction based on the xc attribute. If dispersion is allowed, return the DFTD3 disp version, such as d3bj, d3zero, d4.
 dump_chk(envs_or_file)[source]#
Serialize the SCF object and save it to the specified chkfile.
 Args:
 envs_or_file:
If this argument is a file path, the serialized SCF object is saved to the file specified by this argument. If this attribute is a dict (created by locals()), the necessary variables are saved to the file specified by the attribute mf.chkfile.
 dump_scf_summary(verbose=5)#
 energy_elec(dm=None, h1e=None, vhf=None)#
Electronic part of HartreeFock energy, for given core hamiltonian and HF potential
… math:
E = \sum_{ij}h_{ij} \gamma_{ji} + \frac{1}{2}\sum_{ijkl} \gamma_{ji}\gamma_{lk} \langle ikjl\rangle
Note this function has side effects which cause mf.scf_summary updated.
 Args:
mf : an instance of SCF class
 Kwargs:
 dm2D ndarray
oneparticle density matrix
 h1e2D ndarray
Core hamiltonian
 vhf2D ndarray
HF potential
 Returns:
HartreeFock electronic energy and the Coulomb energy
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> mf = scf.RHF(mol) >>> mf.scf() >>> dm = mf.make_rdm1() >>> scf.hf.energy_elec(mf, dm) (1.5176090667746334, 0.60917167853723675) >>> mf.energy_elec(dm) (1.5176090667746334, 0.60917167853723675)
 energy_tot(dm=None, h1e=None, vhf=None)#
Total HartreeFock energy, electronic part plus nuclear repulsion See
scf.hf.energy_elec()
for the electron partNote this function has side effects which cause mf.scf_summary updated.
 from_chk(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 get_dispersion(disp=None, with_3body=None, verbose=None)#
 get_fock(h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)#
F = h^{core} + V^{HF}
Special treatment (damping, DIIS, or level shift) will be applied to the Fock matrix if diis and cycle is specified (The two parameters are passed to get_fock function during the SCF iteration)
 Kwargs:
 h1e2D ndarray
Core hamiltonian
 s1e2D ndarray
Overlap matrix, for DIIS
 vhf2D ndarray
HF potential matrix
 dm2D ndarray
Density matrix, for DIIS
 cycleint
Then present SCF iteration step, for DIIS
 diisan object of
SCF.DIIS
class DIIS object to hold intermediate Fock and error vectors
 diis_start_cycleint
The step to start DIIS. Default is 0.
 level_shift_factorfloat or int
Level shift (in AU) for virtual space. Default is 0.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_j(mol=None, dm=None, hermi=1, omega=None)[source]#
Compute J matrices for all input density matrices
 get_jk(mol=None, dm=None, hermi=1, with_j=True, with_k=True, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 get_k(mol=None, dm=None, hermi=1, omega=None)[source]#
Compute K matrices for all input density matrices
 get_occ(mo_energy=None, mo_coeff=None)#
Label the occupancies for each orbital
 Kwargs:
 mo_energy1D ndarray
Obital energies
 mo_coeff2D ndarray
Obital coefficients
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 0, 2, 2, 2])
 get_veff(mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1)[source]#
HartreeFock potential matrix for the given density matrix
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
matrix Vhf = 2*J  K. Vhf can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dm0 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf0 = scf.hf.get_veff(mol, dm0, hermi=0) >>> dm1 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf1 = scf.hf.get_veff(mol, dm1, hermi=0) >>> vhf2 = scf.hf.get_veff(mol, dm1, dm_last=dm0, vhf_last=vhf0, hermi=0) >>> numpy.allclose(vhf1, vhf2) True
 init_guess = 'minao'#
 init_guess_by_1e(mol=None)[source]#
Generate initial guess density matrix from core hamiltonian
 Returns:
Density matrix, 2D ndarray
 init_guess_by_atom(mol=None)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 init_guess_by_chkfile(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 init_guess_by_minao(mol=None)[source]#
Generate initial guess density matrix based on ANO basis, then project the density matrix to the basis set defined by
mol
 Returns:
Density matrix, 2D ndarray
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> scf.hf.init_guess_by_minao(mol) array([[ 0.94758917, 0.09227308], [ 0.09227308, 0.94758917]])
 init_guess_by_mod_huckel(updated_rule, mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_sap(mol=None, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 istype(type_code)[source]#
Checks if the object is an instance of the class specified by the type_code. type_code can be a class or a str. If the type_code is a class, it is equivalent to the Python builtin function isinstance. If the type_code is a str, it checks the type_code against the names of the object and all its parent classes.
 kernel(dm0=None, **kwargs)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 level_shift = 0#
 make_rdm1(mo_coeff=None, mo_occ=None, **kwargs)#
Oneparticle density matrix in AO representation
 Args:
 mo_coeff2D ndarray
Orbital coefficients. Each column is one orbital.
 mo_occ1D ndarray
Occupancy
 Returns:
Oneparticle density matrix, 2D ndarray
 make_rdm2(mo_coeff=None, mo_occ=None, **kwargs)#
Twoparticle density matrix in AO representation
 Args:
 mo_coeff2D ndarray
Orbital coefficients. Each column is one orbital.
 mo_occ1D ndarray
Occupancy
 Returns:
Twoparticle density matrix, 4D ndarray
 max_cycle = 50#
 mulliken_meta(mol=None, dm=None, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 mulliken_pop(mol=None, dm=None, s=None, verbose=5)[source]#
Mulliken population analysis
\[M_{ij} = D_{ij} S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\] Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 mulliken_pop_meta_lowdin_ao(*args, **kwargs)#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 property opt#
 pop(*args, **kwargs)[source]#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 quad_moment(mol=None, dm=None, unit='DebyeAngstrom', origin=None, verbose=3, **kwargs)[source]#
Calculates traceless quadrupole moment tensor.
The traceless quadrupole tensor is given by
\[\begin{split}Q_{ij} &=  \frac{1}{2} \sum_{\mu \nu} P_{\mu \nu} \left[ 3 (\nu  r_i r_j  \mu)  \delta_{ij} (\nu  r^2  \mu) \right] \\ &+ \frac{1}{2} \sum_A Q_A \left( R_{iA} R_{jA}  \delta_{ij} \\mathbf{R}_A\^2 \right).\end{split}\]If the molecule has a dipole, the quadrupole moment depends on the location of the origin. By default, the origin is taken to be (0, 0, 0), but it can be set manually via the keyword argument origin.
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices origin : optional; length 3 list, tuple, or 1D arrayLocation of the origin. By default, it is (0, 0, 0).
 Return:
Traceless quadrupole tensor, 2D ndarray.
 sap_basis = 'sapgrasplarge'#
 scf(dm0=None, **kwargs)[source]#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 to_ghf()[source]#
Convert the input meanfield object to a GHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_gks(xc='HF')[source]#
Convert the input meanfield object to a GKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_ks(xc='HF')[source]#
Convert the input meanfield object to the associated KS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_rhf()[source]#
Convert the input meanfield object to a RHF/ROHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_rks(xc='HF')[source]#
Convert the input meanfield object to a RKS/ROKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_uhf()[source]#
Convert the input meanfield object to a UHF object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 to_uks(xc='HF')[source]#
Convert the input meanfield object to a UKS object.
Note this conversion only changes the class of the meanfield object. The total energy and wavefunction are the same as them in the input meanfield object.
 update(chkfile=None)#
Read attributes from the chkfile then replace the attributes of current object. It’s an alias of function update_from_chk_.
 update_(chkfile=None)[source]#
Read attributes from the chkfile then replace the attributes of current object. It’s an alias of function update_from_chk_.
 update_from_chk(chkfile=None)#
Read attributes from the chkfile then replace the attributes of current object. It’s an alias of function update_from_chk_.
 update_from_chk_(chkfile=None)#
Read attributes from the chkfile then replace the attributes of current object. It’s an alias of function update_from_chk_.
 x2c()#
 x2c1e()#
 class pyscf.scf.hf.SCF_Scanner(mf_obj)[source]#
Bases:
SinglePointScanner
 pyscf.scf.hf.analyze(mf, verbose=5, with_meta_lowdin=True, origin=None, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 pyscf.scf.hf.as_scanner(mf)[source]#
Generating a scanner/solver for HF PES.
The returned solver is a function. This function requires one argument “mol” as input and returns total HF energy.
The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the SCF object (DIIS, conv_tol, max_memory etc) are automatically applied in the solver.
Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, …) during calculation.
Examples:
>>> from pyscf import gto, scf >>> hf_scanner = scf.RHF(gto.Mole().set(verbose=0)).as_scanner() >>> hf_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1')) 98.552190448277955 >>> hf_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5')) 98.414750424294368
 pyscf.scf.hf.canonicalize(mf, mo_coeff, mo_occ, fock=None)[source]#
Canonicalization diagonalizes the Fock matrix within occupied, open, virtual subspaces separatedly (without change occupancy).
 pyscf.scf.hf.dip_moment(mol, dm, unit='Debye', origin=None, verbose=3, **kwargs)[source]#
Dipole moment calculation
\[\begin{split}\mu_x = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nux\mu) + \sum_A Q_A X_A\\ \mu_y = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuy\mu) + \sum_A Q_A Y_A\\ \mu_z = \sum_{\mu}\sum_{\nu} P_{\mu\nu}(\nuz\mu) + \sum_A Q_A Z_A\end{split}\]where \(\mu_x, \mu_y, \mu_z\) are the x, y and z components of dipole moment
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices origin : optional; length 3 list, tuple, or 1D arrayLocation of the origin. By default, the point (0, 0, 0) is used.
 Return:
A list: the dipole moment on x, y and z component
 pyscf.scf.hf.dot_eri_dm(eri, dm, hermi=0, with_j=True, with_k=True)[source]#
Compute J, K matrices in terms of the given 2electron integrals and density matrix:
J ~ numpy.einsum(‘pqrs,qp>rs’, eri, dm) K ~ numpy.einsum(‘pqrs,qr>ps’, eri, dm)
 Args:
 erindarray
8fold or 4fold ERIs or complex integral array with N^4 elements (N is the number of orbitals)
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> eri = _vhf.int2e_sph(mol._atm, mol._bas, mol._env) >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.dot_eri_dm(eri, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 pyscf.scf.hf.energy_elec(mf, dm=None, h1e=None, vhf=None)[source]#
Electronic part of HartreeFock energy, for given core hamiltonian and HF potential
… math:
E = \sum_{ij}h_{ij} \gamma_{ji} + \frac{1}{2}\sum_{ijkl} \gamma_{ji}\gamma_{lk} \langle ikjl\rangle
Note this function has side effects which cause mf.scf_summary updated.
 Args:
mf : an instance of SCF class
 Kwargs:
 dm2D ndarray
oneparticle density matrix
 h1e2D ndarray
Core hamiltonian
 vhf2D ndarray
HF potential
 Returns:
HartreeFock electronic energy and the Coulomb energy
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> mf = scf.RHF(mol) >>> mf.scf() >>> dm = mf.make_rdm1() >>> scf.hf.energy_elec(mf, dm) (1.5176090667746334, 0.60917167853723675) >>> mf.energy_elec(dm) (1.5176090667746334, 0.60917167853723675)
 pyscf.scf.hf.energy_tot(mf, dm=None, h1e=None, vhf=None)[source]#
Total HartreeFock energy, electronic part plus nuclear repulsion See
scf.hf.energy_elec()
for the electron partNote this function has side effects which cause mf.scf_summary updated.
 pyscf.scf.hf.get_fock(mf, h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)[source]#
F = h^{core} + V^{HF}
Special treatment (damping, DIIS, or level shift) will be applied to the Fock matrix if diis and cycle is specified (The two parameters are passed to get_fock function during the SCF iteration)
 Kwargs:
 h1e2D ndarray
Core hamiltonian
 s1e2D ndarray
Overlap matrix, for DIIS
 vhf2D ndarray
HF potential matrix
 dm2D ndarray
Density matrix, for DIIS
 cycleint
Then present SCF iteration step, for DIIS
 diisan object of
SCF.DIIS
class DIIS object to hold intermediate Fock and error vectors
 diis_start_cycleint
The step to start DIIS. Default is 0.
 level_shift_factorfloat or int
Level shift (in AU) for virtual space. Default is 0.
 pyscf.scf.hf.get_grad(mo_coeff, mo_occ, fock_ao)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 pyscf.scf.hf.get_hcore(mol)[source]#
Core Hamiltonian
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> scf.hf.get_hcore(mol) array([[0.93767904, 0.59316327], [0.59316327, 0.93767904]])
 pyscf.scf.hf.get_init_guess(mol, key='minao', **kwargs)[source]#
Generate density matrix for initial guess
 Kwargs:
 keystr
One of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’.
 pyscf.scf.hf.get_jk(mol, dm, hermi=1, vhfopt=None, with_j=True, with_k=True, omega=None)[source]#
Compute J, K matrices for all input density matrices
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether J, K matrix is hermitian
0 : not hermitian and not symmetric1 : hermitian or symmetric2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 with_jboolean
Whether to compute J matrices
 with_kboolean
Whether to compute K matrices
 omegafloat
Parameter of rangeseparated Coulomb operator: erf( omega * r12 ) / r12. If specified, integration are evaluated based on the longrange part of the rangeseparated Coulomb operator.
 Returns:
Depending on the given dm, the function returns one J and one K matrix, or a list of J matrices and a list of K matrices, corresponding to the input density matrices.
Examples:
>>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dms = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> j, k = scf.hf.get_jk(mol, dms, hermi=0) >>> print(j.shape) (3, 2, 2)
 pyscf.scf.hf.get_occ(mf, mo_energy=None, mo_coeff=None)[source]#
Label the occupancies for each orbital
 Kwargs:
 mo_energy1D ndarray
Obital energies
 mo_coeff2D ndarray
Obital coefficients
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 0, 2, 2, 2])
 pyscf.scf.hf.get_veff(mol, dm, dm_last=None, vhf_last=None, hermi=1, vhfopt=None)[source]#
HartreeFock potential matrix for the given density matrix
 Args:
mol : an instance of
Mole
 dmndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
matrix Vhf = 2*J  K. Vhf can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> from pyscf.scf import _vhf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dm0 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf0 = scf.hf.get_veff(mol, dm0, hermi=0) >>> dm1 = numpy.random.random((mol.nao_nr(),mol.nao_nr())) >>> vhf1 = scf.hf.get_veff(mol, dm1, hermi=0) >>> vhf2 = scf.hf.get_veff(mol, dm1, dm_last=dm0, vhf_last=vhf0, hermi=0) >>> numpy.allclose(vhf1, vhf2) True
 pyscf.scf.hf.init_guess_by_1e(mol)[source]#
Generate initial guess density matrix from core hamiltonian
 Returns:
Density matrix, 2D ndarray
 pyscf.scf.hf.init_guess_by_atom(mol)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 pyscf.scf.hf.init_guess_by_chkfile(mol, chkfile_name, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 pyscf.scf.hf.init_guess_by_huckel(mol)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 pyscf.scf.hf.init_guess_by_minao(mol)[source]#
Generate initial guess density matrix based on ANO basis, then project the density matrix to the basis set defined by
mol
 Returns:
Density matrix, 2D ndarray
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> scf.hf.init_guess_by_minao(mol) array([[ 0.94758917, 0.09227308], [ 0.09227308, 0.94758917]])
 pyscf.scf.hf.init_guess_by_mod_huckel(mol)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 pyscf.scf.hf.init_guess_by_sap(mol, sap_basis, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 pyscf.scf.hf.kernel(mf, conv_tol=1e10, conv_tol_grad=None, dump_chk=True, dm0=None, callback=None, conv_check=True, **kwargs)[source]#
kernel: the SCF driver.
 Args:
 mfan instance of SCF class
mf object holds all parameters to control SCF. One can modify its member functions to change the behavior of SCF. The member functions which are called in kernel are
mf.get_init_guessmf.get_hcoremf.get_ovlpmf.get_veffmf.get_fockmf.get_gradmf.eigmf.get_occmf.make_rdm1mf.energy_totmf.dump_chk
 Kwargs:
 conv_tolfloat
converge threshold.
 conv_tol_gradfloat
gradients converge threshold.
 dump_chkbool
Whether to save SCF intermediate results in the checkpoint file
 dm0ndarray
Initial guess density matrix. If not given (the default), the kernel takes the density matrix generated by
mf.get_init_guess
. callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. sap_basisstr
SAP basis name
 Returns:
A list : scf_conv, e_tot, mo_energy, mo_coeff, mo_occ
 scf_convbool
True means SCF converged
 e_totfloat
HartreeFock energy of last iteration
 mo_energy1D float array
Orbital energies. Depending the eig function provided by mf object, the orbital energies may NOT be sorted.
 mo_coeff2D array
Orbital coefficients.
 mo_occ1D array
Orbital occupancies. The occupancies may NOT be sorted from large to small.
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> conv, e, mo_e, mo, mo_occ = scf.hf.kernel(scf.hf.SCF(mol), dm0=numpy.eye(mol.nao_nr())) >>> print('conv = %s, E(HF) = %.12f' % (conv, e)) conv = True, E(HF) = 1.081170784378
 pyscf.scf.hf.level_shift(s, d, f, factor)[source]#
Apply level shift \(\Delta\) to virtual orbitals
\begin{align} FC &= SCE \\ F &= F + SC \Lambda C^\dagger S \\ \Lambda_{ij} &= \begin{cases} \delta_{ij}\Delta & i \in \text{virtual} \\ 0 & \text{otherwise} \end{cases} \end{align} Returns:
New Fock matrix, 2D ndarray
 pyscf.scf.hf.make_rdm1(mo_coeff, mo_occ, **kwargs)[source]#
Oneparticle density matrix in AO representation
 Args:
 mo_coeff2D ndarray
Orbital coefficients. Each column is one orbital.
 mo_occ1D ndarray
Occupancy
 Returns:
Oneparticle density matrix, 2D ndarray
 pyscf.scf.hf.make_rdm2(mo_coeff, mo_occ, **kwargs)[source]#
Twoparticle density matrix in AO representation
 Args:
 mo_coeff2D ndarray
Orbital coefficients. Each column is one orbital.
 mo_occ1D ndarray
Occupancy
 Returns:
Twoparticle density matrix, 4D ndarray
 pyscf.scf.hf.make_sap(mol, sap_basis)[source]#
Superposition of atomic potentials (SAP) potential matrix
 Args:
 molMoleBase object
molecule for which SAP is computed
 sap_basisdict
SAP basis
 Returns:
 Vsapndarray
SAP potential matrix
 pyscf.scf.hf.mulliken_meta(mol, dm, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 pyscf.scf.hf.mulliken_pop(mol, dm, s=None, verbose=5)[source]#
Mulliken population analysis
\[M_{ij} = D_{ij} S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\] Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 pyscf.scf.hf.mulliken_pop_meta_lowdin_ao(mol, dm, verbose=5, pre_orth_method='ANO', s=None)#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 pyscf.scf.hf.pack_uniq_var(x, mo_occ)[source]#
Extract the unique variables from the full orbitalgradients (or orbitalrotation) matrix
 pyscf.scf.hf.quad_moment(mol, dm, unit='DebyeAngstrom', origin=None, verbose=3, **kwargs)[source]#
Calculates traceless quadrupole moment tensor.
The traceless quadrupole tensor is given by
\[\begin{split}Q_{ij} &=  \frac{1}{2} \sum_{\mu \nu} P_{\mu \nu} \left[ 3 (\nu  r_i r_j  \mu)  \delta_{ij} (\nu  r^2  \mu) \right] \\ &+ \frac{1}{2} \sum_A Q_A \left( R_{iA} R_{jA}  \delta_{ij} \\mathbf{R}_A\^2 \right).\end{split}\]If the molecule has a dipole, the quadrupole moment depends on the location of the origin. By default, the origin is taken to be (0, 0, 0), but it can be set manually via the keyword argument origin.
 Args:
mol: an instance of
Mole
dm : a 2D ndarrays density matrices origin : optional; length 3 list, tuple, or 1D arrayLocation of the origin. By default, it is (0, 0, 0).
 Return:
Traceless quadrupole tensor, 2D ndarray.
pyscf.scf.hf_symm module#
Nonrelativistic restricted HartreeFock with point group symmetry.
The symmetry are not handled in a separate data structure. Note that during the SCF iteration, the orbitals are grouped in terms of symmetry irreps. But the orbitals in the result are sorted based on the orbital energies. Function symm.label_orb_symm can be used to detect the symmetry of the molecular orbitals.
 class pyscf.scf.hf_symm.HF1e(mol)[source]#
Bases:
SymAdaptedROHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 pyscf.scf.hf_symm.RHF#
alias of
SymAdaptedRHF
 pyscf.scf.hf_symm.ROHF#
alias of
SymAdaptedROHF
 class pyscf.scf.hf_symm.SymAdaptedRHF(mol)[source]#
Bases:
RHF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for symmetry allowed RHF:
 irrep_nelecdict
Specify the number of electrons for particular irrep {‘ir_name’:int,…}. For the irreps not listed in this dict, the program will choose the occupancy based on the orbital energies.
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', symmetry=True, verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() 76.016789472074251 >>> mf.get_irrep_nelec() {'A1': 6, 'A2': 0, 'B1': 2, 'B2': 2} >>> mf.irrep_nelec = {'A2': 2} >>> mf.scf() 72.768201804695622 >>> mf.get_irrep_nelec() {'A1': 6, 'A2': 2, 'B1': 2, 'B2': 0}
 CASCI(*args, **kwargs)#
 CASSCF(*args, **kwargs)#
CASCI/CASSCF
 Args:
 mf_or_molSCF object or Mole object
SCF or Mole to define the problem size.
 ncasint
Number of active orbitals.
 nelecasint or a pair of int
Number of electrons in active space.
 Kwargs:
 ncoreint
Number of doubly occupied core orbitals. If not presented, this parameter can be automatically determined.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
. max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
. ncasint
Active space size.
 nelecastuple of int
Active (nelec_alpha, nelec_beta)
 ncoreint or tuple of int
Core electron number. In UHFCASSCF, it’s a tuple to indicate the different core electron numbers.
 natorbbool
Whether to transform natural orbitals in active space. Note: when CASCI/CASSCF are combined with DMRG solver or selected CI solver, enabling this parameter may slightly change the total energy. False by default.
 canonicalizationbool
Whether to canonicalize orbitals in core and external space against the general Fock matrix. The orbitals in active space are NOT transformed by default. To get the natural orbitals in active space, the attribute .natorb needs to be enabled. True by default.
 sorting_mo_energybool
Whether to sort the orbitals based on the diagonal elements of the general Fock matrix. Default is False.
 fcisolveran instance of
FCISolver
The pyscf.fci module provides several FCISolver for different scenario. Generally, fci.direct_spin1.FCISolver can be used for all RHFCASSCF. However, a proper FCISolver can provide better performance and better numerical stability. One can either use
fci.solver()
function to pick the FCISolver by the program or manually assigen the FCISolver to this attribute, e.g.>>> from pyscf import fci >>> mc = mcscf.CASSCF(mf, 4, 4) >>> mc.fcisolver = fci.solver(mol, singlet=True) >>> mc.fcisolver = fci.direct_spin1.FCISolver(mol)
You can control FCISolver by setting e.g.:
>>> mc.fcisolver.max_cycle = 30 >>> mc.fcisolver.conv_tol = 1e7
For more details of the parameter for FCISolver, See
fci
.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
When canonicalization is specified, the orbitals are canonical orbitals which make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is specified, the active segment of the mo_coeff is natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
 mo_occndarray
Occupation numbers of natural orbitals if natorb is specified.
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASCI(mf, 6, 6) >>> mc.kernel()[0] 108.980200816243354
Extra attributes for CASSCF:
 conv_tolfloat
Converge threshold. Default is 1e7
 conv_tol_gradfloat
Converge threshold for CI gradients and orbital rotation gradients. If not specified, it is set to sqrt(conv_tol).
 max_stepsizefloat
The step size for orbital rotation. Small step (0.005  0.05) is prefered. Default is 0.02.
 max_cycle_macroint
Max number of macro iterations. Default is 50.
 max_cycle_microint
Max number of micro iterations in each macro iteration. Depending on systems, increasing this value might reduce the total macro iterations. Generally, 2  5 steps should be enough. Default is 4.
 small_rot_tolfloat
Threshold for orbital rotation to be considered small. If the largest orbital rotation is smaller than this value, the CI solver will restart from the previous iteration if supported. Default is 0.01
 ah_level_shiftfloat, for AH solver.
Level shift for the Davidson diagonalization in AH solver. Default is 1e8.
 ah_conv_tolfloat, for AH solver.
converge threshold for AH solver. Default is 1e12.
 ah_max_cyclefloat, for AH solver.
Max number of iterations allowd in AH solver. Default is 30.
 ah_lindepfloat, for AH solver.
Linear dependence threshold for AH solver. Default is 1e14.
 ah_start_tolflat, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 2.5.
 ah_start_cycleint, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 3.
ah_conv_tol
,ah_max_cycle
,ah_lindep
,ah_start_tol
andah_start_cycle
can affect the accuracy and performance of CASSCF solver. Lowerah_conv_tol
andah_lindep
might improve the accuracy of CASSCF optimization, but decrease the performance.>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.UHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.conv_tol = 1e10 >>> mc.ah_conv_tol = 1e5 >>> mc.kernel()[0] 109.044401898486001 >>> mc.ah_conv_tol = 1e10 >>> mc.kernel()[0] 109.044401887945668
 chkfilestr
Checkpoint file to save the intermediate orbitals during the CASSCF optimization. Default is the checkpoint file of mean field object.
 ci_response_spaceint
subspace size to solve the CI vector response. Default is 3.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. scale_restorationfloat
When a step of orbital rotation moves out of trust region, the orbital optimization will be restored to previous state and the step size of the orbital rotation needs to be reduced. scale_restoration controls how much to scale down the step size.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
Optimized CASSCF orbitals coefficients. When canonicalization is specified, the returned orbitals make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is enabled, the active segment of mo_coeff is transformed to natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.kernel()[0] 109.044401882238134
 analyze(verbose=None, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Occupancy for each irreps; Mulliken population analysis
 canonicalize(mo_coeff, mo_occ, fock=None)#
Canonicalization diagonalizes the Fock matrix in occupied, open, virtual subspaces separatedly (without change occupancy).
 eig(h, s, symm_orb=None, irrep_id=None)#
Solve generalized eigenvalue problem, for each irrep. The eigenvalues and eigenvectors are not sorted to ascending order. Instead, they are grouped based on irreps.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_irrep_nelec(mol=None, mo_coeff=None, mo_occ=None, s=None)[source]#
Electron numbers for each irreducible representation.
 Args:
 molan instance of
Mole
To provide irrep_id, and spinadapted basis
 mo_coeff2D ndarray
Regular orbital coefficients, without grouping for irreps
 mo_occ1D ndarray
Regular occupancy, without grouping for irreps
 molan instance of
 Returns:
 irrep_nelecdict
The number of electrons for each irrep {‘ir_name’:int,…}.
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', symmetry=True, verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() 76.016789472074251 >>> scf.hf_symm.get_irrep_nelec(mol, mf.mo_coeff, mf.mo_occ) {'A1': 6, 'A2': 0, 'B1': 2, 'B2': 2}
 get_occ(mo_energy=None, mo_coeff=None)[source]#
We assumed mo_energy are grouped by symmetry irreps, (see function self.eig). The orbitals are sorted after SCF.
 get_wfnsym(mo_coeff=None, mo_occ=None, orbsym=None)#
 property orbsym#
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 property wfnsym#
 class pyscf.scf.hf_symm.SymAdaptedROHF(mol)[source]#
Bases:
ROHF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for symmetry allowed ROHF:
 irrep_nelecdict
Specify the number of alpha/beta electrons for particular irrep {‘ir_name’:(int,int), …}. For the irreps not listed in these dicts, the program will choose the occupancy based on the orbital energies.
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', symmetry=True, charge=1, spin=1, verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() 75.619358861084052 >>> mf.get_irrep_nelec() {'A1': (3, 3), 'A2': (0, 0), 'B1': (1, 1), 'B2': (1, 0)} >>> mf.irrep_nelec = {'B1': (1, 0)} >>> mf.scf() 75.425669486776457 >>> mf.get_irrep_nelec() {'A1': (3, 3), 'A2': (0, 0), 'B1': (1, 0), 'B2': (1, 1)}
 CASCI(*args, **kwargs)#
 CASSCF(*args, **kwargs)#
CASCI/CASSCF
 Args:
 mf_or_molSCF object or Mole object
SCF or Mole to define the problem size.
 ncasint
Number of active orbitals.
 nelecasint or a pair of int
Number of electrons in active space.
 Kwargs:
 ncoreint
Number of doubly occupied core orbitals. If not presented, this parameter can be automatically determined.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
. max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
. ncasint
Active space size.
 nelecastuple of int
Active (nelec_alpha, nelec_beta)
 ncoreint or tuple of int
Core electron number. In UHFCASSCF, it’s a tuple to indicate the different core electron numbers.
 natorbbool
Whether to transform natural orbitals in active space. Note: when CASCI/CASSCF are combined with DMRG solver or selected CI solver, enabling this parameter may slightly change the total energy. False by default.
 canonicalizationbool
Whether to canonicalize orbitals in core and external space against the general Fock matrix. The orbitals in active space are NOT transformed by default. To get the natural orbitals in active space, the attribute .natorb needs to be enabled. True by default.
 sorting_mo_energybool
Whether to sort the orbitals based on the diagonal elements of the general Fock matrix. Default is False.
 fcisolveran instance of
FCISolver
The pyscf.fci module provides several FCISolver for different scenario. Generally, fci.direct_spin1.FCISolver can be used for all RHFCASSCF. However, a proper FCISolver can provide better performance and better numerical stability. One can either use
fci.solver()
function to pick the FCISolver by the program or manually assigen the FCISolver to this attribute, e.g.>>> from pyscf import fci >>> mc = mcscf.CASSCF(mf, 4, 4) >>> mc.fcisolver = fci.solver(mol, singlet=True) >>> mc.fcisolver = fci.direct_spin1.FCISolver(mol)
You can control FCISolver by setting e.g.:
>>> mc.fcisolver.max_cycle = 30 >>> mc.fcisolver.conv_tol = 1e7
For more details of the parameter for FCISolver, See
fci
.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
When canonicalization is specified, the orbitals are canonical orbitals which make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is specified, the active segment of the mo_coeff is natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
 mo_occndarray
Occupation numbers of natural orbitals if natorb is specified.
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASCI(mf, 6, 6) >>> mc.kernel()[0] 108.980200816243354
Extra attributes for CASSCF:
 conv_tolfloat
Converge threshold. Default is 1e7
 conv_tol_gradfloat
Converge threshold for CI gradients and orbital rotation gradients. If not specified, it is set to sqrt(conv_tol).
 max_stepsizefloat
The step size for orbital rotation. Small step (0.005  0.05) is prefered. Default is 0.02.
 max_cycle_macroint
Max number of macro iterations. Default is 50.
 max_cycle_microint
Max number of micro iterations in each macro iteration. Depending on systems, increasing this value might reduce the total macro iterations. Generally, 2  5 steps should be enough. Default is 4.
 small_rot_tolfloat
Threshold for orbital rotation to be considered small. If the largest orbital rotation is smaller than this value, the CI solver will restart from the previous iteration if supported. Default is 0.01
 ah_level_shiftfloat, for AH solver.
Level shift for the Davidson diagonalization in AH solver. Default is 1e8.
 ah_conv_tolfloat, for AH solver.
converge threshold for AH solver. Default is 1e12.
 ah_max_cyclefloat, for AH solver.
Max number of iterations allowd in AH solver. Default is 30.
 ah_lindepfloat, for AH solver.
Linear dependence threshold for AH solver. Default is 1e14.
 ah_start_tolflat, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 2.5.
 ah_start_cycleint, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 3.
ah_conv_tol
,ah_max_cycle
,ah_lindep
,ah_start_tol
andah_start_cycle
can affect the accuracy and performance of CASSCF solver. Lowerah_conv_tol
andah_lindep
might improve the accuracy of CASSCF optimization, but decrease the performance.>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.UHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.conv_tol = 1e10 >>> mc.ah_conv_tol = 1e5 >>> mc.kernel()[0] 109.044401898486001 >>> mc.ah_conv_tol = 1e10 >>> mc.kernel()[0] 109.044401887945668
 chkfilestr
Checkpoint file to save the intermediate orbitals during the CASSCF optimization. Default is the checkpoint file of mean field object.
 ci_response_spaceint
subspace size to solve the CI vector response. Default is 3.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. scale_restorationfloat
When a step of orbital rotation moves out of trust region, the orbital optimization will be restored to previous state and the step size of the orbital rotation needs to be reduced. scale_restoration controls how much to scale down the step size.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
Optimized CASSCF orbitals coefficients. When canonicalization is specified, the returned orbitals make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is enabled, the active segment of mo_coeff is transformed to natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.kernel()[0] 109.044401882238134
 TDA = None#
 TDHF = None#
 analyze(verbose=None, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis
 canonicalize(mo_coeff, mo_occ, fock=None)[source]#
Canonicalization diagonalizes the Fock matrix in occupied, open, virtual subspaces separatedly (without change occupancy).
 eig(fock, s)[source]#
Solve generalized eigenvalue problem, for each irrep. The eigenvalues and eigenvectors are not sorted to ascending order. Instead, they are grouped based on irreps.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
ROHF gradients is the offdiagonal block [co + cv + ov], where [ cc co cv ] [ oc oo ov ] [ vc vo vv ]
 get_occ(mo_energy=None, mo_coeff=None)[source]#
Label the occupancies for each orbital. NOTE the occupancies are not assigned based on the orbital energy ordering. The first N orbitals are assigned to be occupied orbitals.
Examples:
>>> mol = gto.M(atom='H 0 0 0; O 0 0 1.1', spin=1) >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 2, 2, 1, 0])
 get_orbsym(mo_coeff=None, s=None)#
 get_wfnsym(mo_coeff=None, mo_occ=None, orbsym=None)#
 make_rdm1(mo_coeff=None, mo_occ=None, **kwargs)[source]#
Oneparticle density matrix. mo_occ is a 1D array, with occupancy 1 or 2.
 property orbsym#
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 property wfnsym#
 pyscf.scf.hf_symm.analyze(mf, verbose=5, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Occupancy for each irreps; Mulliken population analysis
 pyscf.scf.hf_symm.canonicalize(mf, mo_coeff, mo_occ, fock=None)[source]#
Canonicalization diagonalizes the Fock matrix in occupied, open, virtual subspaces separatedly (without change occupancy).
 pyscf.scf.hf_symm.eig(mf, h, s, symm_orb=None, irrep_id=None)[source]#
Solve generalized eigenvalue problem, for each irrep. The eigenvalues and eigenvectors are not sorted to ascending order. Instead, they are grouped based on irreps.
 pyscf.scf.hf_symm.get_irrep_nelec(mol, mo_coeff, mo_occ, s=None)[source]#
Electron numbers for each irreducible representation.
 Args:
 molan instance of
Mole
To provide irrep_id, and spinadapted basis
 mo_coeff2D ndarray
Regular orbital coefficients, without grouping for irreps
 mo_occ1D ndarray
Regular occupancy, without grouping for irreps
 molan instance of
 Returns:
 irrep_nelecdict
The number of electrons for each irrep {‘ir_name’:int,…}.
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', symmetry=True, verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() 76.016789472074251 >>> scf.hf_symm.get_irrep_nelec(mol, mf.mo_coeff, mf.mo_occ) {'A1': 6, 'A2': 0, 'B1': 2, 'B2': 2}
 pyscf.scf.hf_symm.get_orbsym(mol, mo_coeff, s=None, check=False, symm_orb=None, irrep_id=None)[source]#
pyscf.scf.jk module#
General JK contraction function for * arbitrary integrals * 4 different molecules * multiple density matrices * arbitrary basis subset for the 4 indices
 pyscf.scf.jk.get_jk(mols, dms, scripts=['ijkl,ji>kl'], intor='int2e_sph', aosym='s1', comp=None, hermi=0, shls_slice=None, verbose=2, vhfopt=None)[source]#
Compute J/K matrices for the given density matrix
 Args:
mols : an instance of
Mole
or a list of Mole objects dmsndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether the returned J (K) matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian intorstr
2electron integral name. See
getints()
for the complete list of available 2electron integral names aosymint or str
Permutation symmetry for the AO integrals
4 or ‘4’ or ‘s4’: 4fold symmetry (default)‘2ij’ or ‘s2ij’ : symmetry between i, j in (ijkl)‘2kl’ or ‘s2kl’ : symmetry between k, l in (ijkl)1 or ‘1’ or ‘s1’: no symmetry‘a4ij’ : 4fold symmetry with antisymmetry between i, j in (ijkl)‘a4kl’ : 4fold symmetry with antisymmetry between k, l in (ijkl)‘a2ij’ : antisymmetry between i, j in (ijkl)‘a2kl’ : antisymmetry between k, l in (ijkl) compint
Components of the integrals, e.g. cint2e_ip_sph has 3 components.
 scriptsstring or a list of strings
Contraction description (following numpy.einsum convention) based on letters [ijkl]. Each script will be onetoone applied to each entry of dms. So it must have the same number of elements as the dms, len(scripts) == len(dms).
 shls_slice8element list
(ish_start, ish_end, jsh_start, jsh_end, ksh_start, ksh_end, lsh_start, lsh_end)
 Returns:
Depending on the number of density matrices, the function returns one J/K matrix or a list of J/K matrices (the same number of entries as the input dms). Each JK matrices may be a 2D array or 3D array if the AO integral has multiple components.
Examples:
>>> from pyscf import gto >>> mol = gto.M(atom='H 0 .5 0; H 0 .5 0', basis='ccpvdz') >>> nao = mol.nao_nr() >>> dm = numpy.random.random((nao,nao)) >>> # Default, Coulomb matrix >>> vj = get_jk(mol, dm) >>> # Coulomb matrix with 8fold permutation symmetry for AO integrals >>> vj = get_jk(mol, dm, 'ijkl,ji>kl', aosym='s8') >>> # Exchange matrix with 8fold permutation symmetry for AO integrals >>> vk = get_jk(mol, dm, 'ijkl,jk>il', aosym='s8') >>> # Compute coulomb and exchange matrices together >>> vj, vk = get_jk(mol, (dm,dm), ('ijkl,ji>kl','ijkl,li>kj'), aosym='s8') >>> # Analytical gradients for coulomb matrix >>> j1 = get_jk(mol, dm, 'ijkl,lk>ij', intor='int2e_ip1_sph', aosym='s2kl', comp=3)
>>> # contraction across two molecules >>> mol1 = gto.M(atom='He 2 0 0', basis='631g') >>> nao1 = mol1.nao_nr() >>> dm1 = numpy.random.random((nao1,nao1)) >>> # Coulomb interaction between two molecules, note 4fold symmetry can be applied >>> jcross = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', aosym='s4') >>> ecoul = numpy.einsum('ij,ij', jcross, dm1) >>> # Exchange interaction between two molecules, no symmetry can be used >>> kcross = get_jk((mol1,mol,mol,mol1), dm, scripts='ijkl,jk>il') >>> ex = numpy.einsum('ij,ji', kcross, dm1)
>>> # Analytical gradients for coulomb matrix between two molecules >>> jcros1 = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', intor='int2e_ip1_sph', comp=3) >>> # Analytical gradients for coulomb interaction between 1s density and the other molecule >>> jpart1 = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', intor='int2e_ip1_sph', comp=3, ... shls_slice=(0,1,0,1,0,mol.nbas,0,mol.nbas))
 pyscf.scf.jk.jk_build(mols, dms, scripts=['ijkl,ji>kl'], intor='int2e_sph', aosym='s1', comp=None, hermi=0, shls_slice=None, verbose=2, vhfopt=None)#
Compute J/K matrices for the given density matrix
 Args:
mols : an instance of
Mole
or a list of Mole objects dmsndarray or list of ndarrays
A density matrix or a list of density matrices
 Kwargs:
 hermiint
Whether the returned J (K) matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian intorstr
2electron integral name. See
getints()
for the complete list of available 2electron integral names aosymint or str
Permutation symmetry for the AO integrals
4 or ‘4’ or ‘s4’: 4fold symmetry (default)‘2ij’ or ‘s2ij’ : symmetry between i, j in (ijkl)‘2kl’ or ‘s2kl’ : symmetry between k, l in (ijkl)1 or ‘1’ or ‘s1’: no symmetry‘a4ij’ : 4fold symmetry with antisymmetry between i, j in (ijkl)‘a4kl’ : 4fold symmetry with antisymmetry between k, l in (ijkl)‘a2ij’ : antisymmetry between i, j in (ijkl)‘a2kl’ : antisymmetry between k, l in (ijkl) compint
Components of the integrals, e.g. cint2e_ip_sph has 3 components.
 scriptsstring or a list of strings
Contraction description (following numpy.einsum convention) based on letters [ijkl]. Each script will be onetoone applied to each entry of dms. So it must have the same number of elements as the dms, len(scripts) == len(dms).
 shls_slice8element list
(ish_start, ish_end, jsh_start, jsh_end, ksh_start, ksh_end, lsh_start, lsh_end)
 Returns:
Depending on the number of density matrices, the function returns one J/K matrix or a list of J/K matrices (the same number of entries as the input dms). Each JK matrices may be a 2D array or 3D array if the AO integral has multiple components.
Examples:
>>> from pyscf import gto >>> mol = gto.M(atom='H 0 .5 0; H 0 .5 0', basis='ccpvdz') >>> nao = mol.nao_nr() >>> dm = numpy.random.random((nao,nao)) >>> # Default, Coulomb matrix >>> vj = get_jk(mol, dm) >>> # Coulomb matrix with 8fold permutation symmetry for AO integrals >>> vj = get_jk(mol, dm, 'ijkl,ji>kl', aosym='s8') >>> # Exchange matrix with 8fold permutation symmetry for AO integrals >>> vk = get_jk(mol, dm, 'ijkl,jk>il', aosym='s8') >>> # Compute coulomb and exchange matrices together >>> vj, vk = get_jk(mol, (dm,dm), ('ijkl,ji>kl','ijkl,li>kj'), aosym='s8') >>> # Analytical gradients for coulomb matrix >>> j1 = get_jk(mol, dm, 'ijkl,lk>ij', intor='int2e_ip1_sph', aosym='s2kl', comp=3)
>>> # contraction across two molecules >>> mol1 = gto.M(atom='He 2 0 0', basis='631g') >>> nao1 = mol1.nao_nr() >>> dm1 = numpy.random.random((nao1,nao1)) >>> # Coulomb interaction between two molecules, note 4fold symmetry can be applied >>> jcross = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', aosym='s4') >>> ecoul = numpy.einsum('ij,ij', jcross, dm1) >>> # Exchange interaction between two molecules, no symmetry can be used >>> kcross = get_jk((mol1,mol,mol,mol1), dm, scripts='ijkl,jk>il') >>> ex = numpy.einsum('ij,ji', kcross, dm1)
>>> # Analytical gradients for coulomb matrix between two molecules >>> jcros1 = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', intor='int2e_ip1_sph', comp=3) >>> # Analytical gradients for coulomb interaction between 1s density and the other molecule >>> jpart1 = get_jk((mol1,mol1,mol,mol), dm, scripts='ijkl,lk>ij', intor='int2e_ip1_sph', comp=3, ... shls_slice=(0,1,0,1,0,mol.nbas,0,mol.nbas))
pyscf.scf.rohf module#
Restricted Openshell HartreeFock
 class pyscf.scf.rohf.HF1e(mol)[source]#
Bases:
ROHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 class pyscf.scf.rohf.ROHF(mol)[source]#
Bases:
RHF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 CASCI(*args, **kwargs)#
 CASSCF(*args, **kwargs)#
CASCI/CASSCF
 Args:
 mf_or_molSCF object or Mole object
SCF or Mole to define the problem size.
 ncasint
Number of active orbitals.
 nelecasint or a pair of int
Number of electrons in active space.
 Kwargs:
 ncoreint
Number of doubly occupied core orbitals. If not presented, this parameter can be automatically determined.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
. max_memoryfloat or int
Allowed memory in MB. Default value equals to
Mole.max_memory
. ncasint
Active space size.
 nelecastuple of int
Active (nelec_alpha, nelec_beta)
 ncoreint or tuple of int
Core electron number. In UHFCASSCF, it’s a tuple to indicate the different core electron numbers.
 natorbbool
Whether to transform natural orbitals in active space. Note: when CASCI/CASSCF are combined with DMRG solver or selected CI solver, enabling this parameter may slightly change the total energy. False by default.
 canonicalizationbool
Whether to canonicalize orbitals in core and external space against the general Fock matrix. The orbitals in active space are NOT transformed by default. To get the natural orbitals in active space, the attribute .natorb needs to be enabled. True by default.
 sorting_mo_energybool
Whether to sort the orbitals based on the diagonal elements of the general Fock matrix. Default is False.
 fcisolveran instance of
FCISolver
The pyscf.fci module provides several FCISolver for different scenario. Generally, fci.direct_spin1.FCISolver can be used for all RHFCASSCF. However, a proper FCISolver can provide better performance and better numerical stability. One can either use
fci.solver()
function to pick the FCISolver by the program or manually assigen the FCISolver to this attribute, e.g.>>> from pyscf import fci >>> mc = mcscf.CASSCF(mf, 4, 4) >>> mc.fcisolver = fci.solver(mol, singlet=True) >>> mc.fcisolver = fci.direct_spin1.FCISolver(mol)
You can control FCISolver by setting e.g.:
>>> mc.fcisolver.max_cycle = 30 >>> mc.fcisolver.conv_tol = 1e7
For more details of the parameter for FCISolver, See
fci
.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
When canonicalization is specified, the orbitals are canonical orbitals which make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is specified, the active segment of the mo_coeff is natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
 mo_occndarray
Occupation numbers of natural orbitals if natorb is specified.
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASCI(mf, 6, 6) >>> mc.kernel()[0] 108.980200816243354
Extra attributes for CASSCF:
 conv_tolfloat
Converge threshold. Default is 1e7
 conv_tol_gradfloat
Converge threshold for CI gradients and orbital rotation gradients. If not specified, it is set to sqrt(conv_tol).
 max_stepsizefloat
The step size for orbital rotation. Small step (0.005  0.05) is prefered. Default is 0.02.
 max_cycle_macroint
Max number of macro iterations. Default is 50.
 max_cycle_microint
Max number of micro iterations in each macro iteration. Depending on systems, increasing this value might reduce the total macro iterations. Generally, 2  5 steps should be enough. Default is 4.
 small_rot_tolfloat
Threshold for orbital rotation to be considered small. If the largest orbital rotation is smaller than this value, the CI solver will restart from the previous iteration if supported. Default is 0.01
 ah_level_shiftfloat, for AH solver.
Level shift for the Davidson diagonalization in AH solver. Default is 1e8.
 ah_conv_tolfloat, for AH solver.
converge threshold for AH solver. Default is 1e12.
 ah_max_cyclefloat, for AH solver.
Max number of iterations allowd in AH solver. Default is 30.
 ah_lindepfloat, for AH solver.
Linear dependence threshold for AH solver. Default is 1e14.
 ah_start_tolflat, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 2.5.
 ah_start_cycleint, for AH solver.
In AH solver, the orbital rotation is started without completely solving the AH problem. This value is to control the start point. Default is 3.
ah_conv_tol
,ah_max_cycle
,ah_lindep
,ah_start_tol
andah_start_cycle
can affect the accuracy and performance of CASSCF solver. Lowerah_conv_tol
andah_lindep
might improve the accuracy of CASSCF optimization, but decrease the performance.>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.UHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.conv_tol = 1e10 >>> mc.ah_conv_tol = 1e5 >>> mc.kernel()[0] 109.044401898486001 >>> mc.ah_conv_tol = 1e10 >>> mc.kernel()[0] 109.044401887945668
 chkfilestr
Checkpoint file to save the intermediate orbitals during the CASSCF optimization. Default is the checkpoint file of mean field object.
 ci_response_spaceint
subspace size to solve the CI vector response. Default is 3.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. scale_restorationfloat
When a step of orbital rotation moves out of trust region, the orbital optimization will be restored to previous state and the step size of the orbital rotation needs to be reduced. scale_restoration controls how much to scale down the step size.
Saved results
 e_totfloat
Total MCSCF energy (electronic energy plus nuclear repulsion)
 e_casfloat
CAS space FCI energy
 cindarray
CAS space FCI coefficients
 mo_coeffndarray
Optimized CASSCF orbitals coefficients. When canonicalization is specified, the returned orbitals make the general Fock matrix (Fock operator on top of MCSCF 1particle density matrix) diagonalized within each subspace (core, active, external). If natorb (natural orbitals in active space) is enabled, the active segment of mo_coeff is transformed to natural orbitals.
 mo_energyndarray
Diagonal elements of general Fock matrix (in mo_coeff representation).
Examples:
>>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0) >>> mf = scf.RHF(mol) >>> mf.scf() >>> mc = mcscf.CASSCF(mf, 6, 6) >>> mc.kernel()[0] 109.044401882238134
 CISD = None#
 DFMP2 = None#
 Gradients(*args, **kwargs)#
Nonrelativistic restricted openshell HartreeFock gradients
 MP2 = None#
 TDA = None#
 TDHF = None#
 analyze(verbose=None, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis
 canonicalize(mo_coeff, mo_occ, fock=None)#
Canonicalization diagonalizes the Fock matrix within occupied, open, virtual subspaces separatedly (without change occupancy).
 check_sanity()#
Check input of class/object attributes, check whether a class method is overwritten. It does not check the attributes which are prefixed with “_”. The return value of method set is the object itself. This allows a series of functions/methods to be executed in pipe.
 convert_from_(mf)#
Convert the input meanfield object to RHF/ROHF
 energy_elec(dm=None, h1e=None, vhf=None)#
Electronic part of HartreeFock energy, for given core hamiltonian and HF potential
… math:
E = \sum_{ij}h_{ij} \gamma_{ji} + \frac{1}{2}\sum_{ijkl} \gamma_{ji}\gamma_{lk} \langle ikjl\rangle
Note this function has side effects which cause mf.scf_summary updated.
 Args:
mf : an instance of SCF class
 Kwargs:
 dm2D ndarray
oneparticle density matrix
 h1e2D ndarray
Core hamiltonian
 vhf2D ndarray
HF potential
 Returns:
HartreeFock electronic energy and the Coulomb energy
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> mf = scf.RHF(mol) >>> mf.scf() >>> dm = mf.make_rdm1() >>> scf.hf.energy_elec(mf, dm) (1.5176090667746334, 0.60917167853723675) >>> mf.energy_elec(dm) (1.5176090667746334, 0.60917167853723675)
 gen_response(mo_coeff=None, mo_occ=None, with_j=True, hermi=0, max_memory=None)#
Generate a function to compute the product of UHF response function and UHF density matrices.
 get_fock(h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)#
Build fock matrix based on Roothaan’s effective fock. See also
get_roothaan_fock()
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
ROHF gradients is the offdiagonal block [co + cv + ov], where [ cc co cv ] [ oc oo ov ] [ vc vo vv ]
 get_init_guess(mol=None, key='minao', **kwargs)#
 get_occ(mo_energy=None, mo_coeff=None)#
Label the occupancies for each orbital. NOTE the occupancies are not assigned based on the orbital energy ordering. The first N orbitals are assigned to be occupied orbitals.
Examples:
>>> mol = gto.M(atom='H 0 0 0; O 0 0 1.1', spin=1) >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 2, 2, 1, 0])
 get_veff(mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1)[source]#
Unrestricted HartreeFock potential matrix of alpha and beta spins, for the given density matrix
\[\begin{split}V_{ij}^\alpha &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\alpha \\ V_{ij}^\beta &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\beta\end{split}\] Args:
mol : an instance of
Mole
 dma list of ndarrays
A list of density matrices, stored as (alpha,alpha,…,beta,beta,…)
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. When it is not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
\(V_{hf} = (V^\alpha, V^\beta)\). \(V^\alpha\) (and \(V^\beta\)) can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dmsa = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dmsb = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dms = numpy.vstack((dmsa,dmsb)) >>> dms.shape (6, 2, 2) >>> vhfa, vhfb = scf.uhf.get_veff(mol, dms, hermi=0) >>> vhfa.shape (3, 2, 2) >>> vhfb.shape (3, 2, 2)
 init_guess_by_1e(mol=None)[source]#
Generate initial guess density matrix from core hamiltonian
 Returns:
Density matrix, 2D ndarray
 init_guess_by_atom(mol=None)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 init_guess_by_chkfile(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 init_guess_by_minao(mol=None)[source]#
Generate initial guess density matrix based on ANO basis, then project the density matrix to the basis set defined by
mol
 Returns:
Density matrix, 2D ndarray
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> scf.hf.init_guess_by_minao(mol) array([[ 0.94758917, 0.09227308], [ 0.09227308, 0.94758917]])
 init_guess_by_mod_huckel(mol=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_sap(mol=None, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 make_rdm1(mo_coeff=None, mo_occ=None, **kwargs)[source]#
Oneparticle density matrix. mo_occ is a 1D array, with occupancy 1 or 2.
 property nelec#
 property nelectron_alpha#
 stability(internal=True, external=False, verbose=None, return_status=False, **kwargs)[source]#
ROHF/ROKS stability analysis.
See also pyscf.scf.stability.rohf_stability function.
 Kwargs:
 internalbool
Internal stability, within the RHF optimization space.
 externalbool
External stability. It is not available in current version.
 return_status: bool
Whether to return stable_i and stable_e
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 pyscf.scf.rohf.analyze(mf, verbose=5, with_meta_lowdin=True, origin=None, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis
 pyscf.scf.rohf.canonicalize(mf, mo_coeff, mo_occ, fock=None)[source]#
Canonicalization diagonalizes the Fock matrix within occupied, open, virtual subspaces separatedly (without change occupancy).
 pyscf.scf.rohf.get_fock(mf, h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)[source]#
Build fock matrix based on Roothaan’s effective fock. See also
get_roothaan_fock()
 pyscf.scf.rohf.get_grad(mo_coeff, mo_occ, fock)[source]#
ROHF gradients is the offdiagonal block [co + cv + ov], where [ cc co cv ] [ oc oo ov ] [ vc vo vv ]
 pyscf.scf.rohf.get_occ(mf, mo_energy=None, mo_coeff=None)[source]#
Label the occupancies for each orbital. NOTE the occupancies are not assigned based on the orbital energy ordering. The first N orbitals are assigned to be occupied orbitals.
Examples:
>>> mol = gto.M(atom='H 0 0 0; O 0 0 1.1', spin=1) >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 2, 2, 1, 0])
 pyscf.scf.rohf.get_roothaan_fock(focka_fockb, dma_dmb, s)[source]#
Roothaan’s effective fock. Ref. http://wwwtheor.ch.cam.ac.uk/people/ross/thesis/node15.html
space
closed
open
virtual
closed
Fc
Fb
Fc
open
Fb
Fc
Fa
virtual
Fc
Fa
Fc
where Fc = (Fa + Fb) / 2
 Returns:
Roothaan effective Fock matrix
 pyscf.scf.rohf.init_guess_by_chkfile(mol, chkfile_name, project=None)[source]#
Read SCF chkfile and make the density matrix for ROHF initial guess.
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
pyscf.scf.stability module#
Wave Function Stability Analysis
Ref. JCP 66, 3045 (1977); DOI:10.1063/1.434318 JCP 104, 9047 (1996); DOI:10.1063/1.471637
See also tddft/rhf.py and scf/newton_ah.py
 pyscf.scf.stability.dhf_stability(mf, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
Stability analysis for DHF/DKS method.
 Args:
mf : DHF or DKS object
 Kwargs:
 return_status: bool
Whether to return stable_i and stable_e
 nrootsint
Number of roots solved by Davidson solver
 tolfloat
Convergence threshold for Davidson solver
 Returns:
If return_status is False (default), the return value includes a new set of orbitals, which are more close to the stable condition.
Else, another one boolean variable (indicating current status: stable or unstable) is returned.
 pyscf.scf.stability.ghf_stability(mf, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
Stability analysis for GHF/GKS method.
 Args:
mf : GHF or GKS object
 Kwargs:
 return_status: bool
Whether to return stable_i and stable_e
 nrootsint
Number of roots solved by Davidson solver
 tolfloat
Convergence threshold for Davidson solver
 Returns:
If return_status is False (default), the return value includes a new set of orbitals, which are more close to the stable condition.
Else, another one boolean variable (indicating current status: stable or unstable) is returned.
 pyscf.scf.stability.rhf_external(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.rhf_internal(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.rhf_stability(mf, internal=True, external=False, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
Stability analysis for RHF/RKS method.
 Args:
mf : RHF or RKS object
 Kwargs:
 internalbool
Internal stability, within the RHF space.
 externalbool
External stability. Including the RHF > UHF and real > complex stability analysis.
 return_status: bool
Whether to return stable_i and stable_e
 nrootsint
Number of roots solved by Davidson solver
 tolfloat
Convergence threshold for Davidson solver
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 pyscf.scf.stability.rohf_external(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.rohf_internal(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.rohf_stability(mf, internal=True, external=False, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
Stability analysis for ROHF/ROKS method.
 Args:
mf : ROHF or ROKS object
 Kwargs:
 internalbool
Internal stability, within the RHF space.
 externalbool
External stability. It is not available in current version.
 return_status: bool
Whether to return stable_i and stable_e
 nrootsint
Number of roots solved by Davidson solver
 tolfloat
Convergence threshold for Davidson solver
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 pyscf.scf.stability.uhf_external(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.uhf_internal(mf, with_symmetry=True, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
 pyscf.scf.stability.uhf_stability(mf, internal=True, external=False, verbose=None, return_status=False, nroots=3, tol=0.0001)[source]#
Stability analysis for UHF/UKS method.
 Args:
mf : UHF or UKS object
 Kwargs:
 internalbool
Internal stability, within the UHF space.
 externalbool
External stability. Including the UHF > GHF and real > complex stability analysis.
 return_status: bool
Whether to return stable_i and stable_e
 nrootsint
Number of roots solved by Davidson solver
 tolfloat
Convergence threshold for Davidson solver
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
pyscf.scf.stability_slow module#
Wave Function Stability Analysis
Ref. JCP, 66, 3045 (1977); DOI:10.1063/1.434318 JCP 104, 9047 (1996); DOI:10.1063/1.471637
pyscf.scf.ucphf module#
Unrestricted coupled perturbed HartreeFock solver
 pyscf.scf.ucphf.kernel(fvind, mo_energy, mo_occ, h1, s1=None, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)#
 Args:
 fvindfunction
Given density matrix, compute (ijkl)D_{lk}*2  (ijkl)D_{jk}
 pyscf.scf.ucphf.solve(fvind, mo_energy, mo_occ, h1, s1=None, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
 Args:
 fvindfunction
Given density matrix, compute (ijkl)D_{lk}*2  (ijkl)D_{jk}
 pyscf.scf.ucphf.solve_nos1(fvind, mo_energy, mo_occ, h1, max_cycle=20, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
For field independent basis. First order overlap matrix is zero
 pyscf.scf.ucphf.solve_withs1(fvind, mo_energy, mo_occ, h1, s1, max_cycle=50, tol=1e09, hermi=False, verbose=2, level_shift=0)[source]#
For field dependent basis. First order overlap matrix is nonzero. The first order orbitals are set to C^1_{ij} = 1/2 S1 e1 = h1  s1*e0 + (e0_je0_i)*c1 + vhf[c1]
pyscf.scf.uhf module#
 class pyscf.scf.uhf.HF1e(mol)[source]#
Bases:
UHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 spin_square(mo_coeff=None, s=None)[source]#
Spin square and multiplicity of UHF determinant
\[S^2 = \frac{1}{2}(S_+ S_ + S_ S_+) + S_z^2\]where \(S_+ = \sum_i S_{i+}\) is effective for all beta occupied orbitals; \(S_ = \sum_i S_{i}\) is effective for all alpha occupied orbitals.
 There are two possibilities for \(S_+ S_\)
same electron \(S_+ S_ = \sum_i s_{i+} s_{i}\),
\[\sum_i \langle UHFs_{i+} s_{i}UHF\rangle = \sum_{pq}\langle ps_+s_q\rangle \gamma_{qp} = n_\alpha\]2) different electrons \(S_+ S_ = \sum s_{i+} s_{j}, (i\neq j)\). There are in total \(n(n1)\) terms. As a twoparticle operator,
\[\langle S_+ S_ \rangle = \langle ijs_+ s_ij\rangle  \langle ijs_+ s_ji\rangle = \langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle\]
 Similarly, for \(S_ S_+\)
same electron
\[\sum_i \langle s_{i} s_{i+}\rangle = n_\beta\]different electrons
\[\langle S_ S_+ \rangle = \langle i^\betaj^\alpha\rangle \langle j^\alphai^\beta\rangle\]
 For \(S_z^2\)
same electron
\[\langle s_z^2\rangle = \frac{1}{4}(n_\alpha + n_\beta)\]different electrons
\[\begin{split}&\frac{1}{2}\sum_{ij}(\langle ij2s_{z1}s_{z2}ij\rangle \langle ij2s_{z1}s_{z2}ji\rangle) \\ &=\frac{1}{4}(\langle i^\alphai^\alpha\rangle \langle j^\alphaj^\alpha\rangle  \langle i^\alphai^\alpha\rangle \langle j^\betaj^\beta\rangle  \langle i^\betai^\beta\rangle \langle j^\alphaj^\alpha\rangle + \langle i^\betai^\beta\rangle \langle j^\betaj^\beta\rangle) \\ &\frac{1}{4}(\langle i^\alphaj^\alpha\rangle \langle j^\alphai^\alpha\rangle + \langle i^\betaj^\beta\rangle\langle j^\betai^\beta\rangle) \\ &=\frac{1}{4}(n_\alpha^2  n_\alpha n_\beta  n_\beta n_\alpha + n_\beta^2) \frac{1}{4}(n_\alpha + n_\beta) \\ &=\frac{1}{4}((n_\alphan_\beta)^2  (n_\alpha+n_\beta))\end{split}\]
In total
\[\begin{split}\langle S^2\rangle &= \frac{1}{2} (n_\alpha\sum_{ij}\langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle +n_\beta \sum_{ij}\langle i^\betaj^\alpha\rangle\langle j^\alphai^\beta\rangle) + \frac{1}{4}(n_\alphan_\beta)^2 \\\end{split}\] Args:
 moa list of 2 ndarrays
Occupied alpha and occupied beta orbitals
 Kwargs:
 sndarray
AO overlap
 Returns:
A list of two floats. The first is the expectation value of S^2. The second is the corresponding 2S+1
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> mo = (mf.mo_coeff[0][:,mf.mo_occ[0]>0], mf.mo_coeff[1][:,mf.mo_occ[1]>0]) >>> print('S^2 = %.7f, 2S+1 = %.7f' % spin_square(mo, mol.intor('int1e_ovlp_sph'))) S^2 = 0.7570150, 2S+1 = 2.0070027
 class pyscf.scf.uhf.UHF(mol)[source]#
Bases:
SCF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for UHF:
 nelec(int, int)
If given, freeze the number of (alpha,beta) electrons to the given value.
 level_shiftnumber or twoelement list
level shift (in Eh) for alpha and beta Fock if twoelement list is given.
 init_guess_breaksymint
This configuration controls the algorithm used to break the spin symmetry of the initial guess:  0 to disable symmetry breaking in the initial guess.  1 to use the default algorithm introduced in pyscf1.7.  2 to adjust the num. electrons for spinup and spindown density matrices (issue #1839).
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> print('S^2 = %.7f, 2S+1 = %.7f' % mf.spin_square()) S^2 = 0.7570150, 2S+1 = 2.0070027
 CASCI = None#
 CASSCF = None#
 CISD(*args, **kwargs)#
 DFMP2 = None#
 Gradients(*args, **kwargs)#
Nonrelativistic unrestricted HartreeFock gradients
 MP2(*args, **kwargs)#
 TDA(*args, **kwargs)#
TammDancoff approximation
 Attributes:
 conv_tolfloat
Diagonalization convergence tolerance. Default is 1e9.
 nstatesint
Number of TD states to be computed. Default is 3.
Saved results:
 convergedbool
Diagonalization converged or not
 e1D array
excitation energy for each excited state.
 xyA list of two 2D arrays
The two 2D arrays are Excitation coefficients X (shape [nocc,nvir]) and deexcitation coefficients Y (shape [nocc,nvir]) for each excited state. (X,Y) are normalized to 1/2 in RHF/RKS methods and normalized to 1 for UHF/UKS methods. In the TDA calculation, Y = 0.
 TDHF(*args, **kwargs)#
 analyze(verbose=None, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Diople moment.
 canonicalize(mo_coeff, mo_occ, fock=None)#
Canonicalization diagonalizes the UHF Fock matrix within occupied, virtual subspaces separatedly (without change occupancy).
 det_ovlp(mo1, mo2, occ1, occ2, ovlp=None)[source]#
Calculate the overlap between two different determinants. It is the product of single values of molecular orbital overlap matrix.
\[S_{12} = \langle \Psi_A  \Psi_B \rangle = (\mathrm{det}\mathbf{U}) (\mathrm{det}\mathbf{V^\dagger}) \prod\limits_{i=1}\limits^{2N} \lambda_{ii}\]where \(\mathbf{U}, \mathbf{V}, \lambda\) are unitary matrices and single values generated by single value decomposition(SVD) of the overlap matrix \(\mathbf{O}\) which is the overlap matrix of two sets of molecular orbitals:
\[\mathbf{U}^\dagger \mathbf{O} \mathbf{V} = \mathbf{\Lambda}\] Args:
 mo1, mo22D ndarrays
Molecualr orbital coefficients
 occ1, occ2: 2D ndarrays
occupation numbers
 Return:
 A list:
the product of single values: float (x_a, x_b): 1D ndarrays \(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\) They are used to calculate asymmetric density matrix
 energy_elec(dm=None, h1e=None, vhf=None)#
Electronic energy of Unrestricted HartreeFock
Note this function has side effects which cause mf.scf_summary updated.
 Returns:
HartreeFock electronic energy and the 2electron part contribution
 gen_response(mo_coeff=None, mo_occ=None, with_j=True, hermi=0, max_memory=None)#
Generate a function to compute the product of UHF response function and UHF density matrices.
 get_fock(h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)#
F = h^{core} + V^{HF}
Special treatment (damping, DIIS, or level shift) will be applied to the Fock matrix if diis and cycle is specified (The two parameters are passed to get_fock function during the SCF iteration)
 Kwargs:
 h1e2D ndarray
Core hamiltonian
 s1e2D ndarray
Overlap matrix, for DIIS
 vhf2D ndarray
HF potential matrix
 dm2D ndarray
Density matrix, for DIIS
 cycleint
Then present SCF iteration step, for DIIS
 diisan object of
SCF.DIIS
class DIIS object to hold intermediate Fock and error vectors
 diis_start_cycleint
The step to start DIIS. Default is 0.
 level_shift_factorfloat or int
Level shift (in AU) for virtual space. Default is 0.
 get_grad(mo_coeff, mo_occ, fock=None)[source]#
RHF orbital gradients
 Args:
 mo_coeff2D ndarray
Obital coefficients
 mo_occ1D ndarray
Orbital occupancy
 fock_ao2D ndarray
Fock matrix in AO representation
 Returns:
Gradients in MO representation. It’s a num_occ*num_vir vector.
 get_jk(mol=None, dm=None, hermi=1, with_j=True, with_k=True, omega=None)[source]#
Coulomb (J) and exchange (K)
 Args:
 dma list of 2D arrays or a list of 3D arrays
(alpha_dm, beta_dm) or (alpha_dms, beta_dms)
 get_occ(mo_energy=None, mo_coeff=None)#
Label the occupancies for each orbital
 Kwargs:
 mo_energy1D ndarray
Obital energies
 mo_coeff2D ndarray
Obital coefficients
Examples:
>>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> energy = numpy.array([10., 1., 1, 2., 0, 3]) >>> mf.get_occ(energy) array([2, 2, 0, 2, 2, 2])
 get_veff(mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1)[source]#
Unrestricted HartreeFock potential matrix of alpha and beta spins, for the given density matrix
\[\begin{split}V_{ij}^\alpha &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\alpha \\ V_{ij}^\beta &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\beta\end{split}\] Args:
mol : an instance of
Mole
 dma list of ndarrays
A list of density matrices, stored as (alpha,alpha,…,beta,beta,…)
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. When it is not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
\(V_{hf} = (V^\alpha, V^\beta)\). \(V^\alpha\) (and \(V^\beta\)) can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dmsa = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dmsb = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dms = numpy.vstack((dmsa,dmsb)) >>> dms.shape (6, 2, 2) >>> vhfa, vhfb = scf.uhf.get_veff(mol, dms, hermi=0) >>> vhfa.shape (3, 2, 2) >>> vhfb.shape (3, 2, 2)
 init_guess_breaksym = 1#
 init_guess_by_1e(mol=None, breaksym=None)[source]#
Generate initial guess density matrix from core hamiltonian
 Returns:
Density matrix, 2D ndarray
 init_guess_by_atom(mol=None, breaksym=None)[source]#
Generate initial guess density matrix from superposition of atomic HF density matrix. The atomic HF is occupancy averaged RHF
 Returns:
Density matrix, 2D ndarray
 init_guess_by_chkfile(chkfile=None, project=None)[source]#
Read the HF results from checkpoint file, then project it to the basis defined by
mol
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_huckel(mol=None, breaksym=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
 Returns:
Density matrix, 2D ndarray
 init_guess_by_minao(mol=None, breaksym=None)[source]#
Initial guess in terms of the overlap to minimal basis.
 init_guess_by_mod_huckel(mol=None, breaksym=None)[source]#
Generate initial guess density matrix from a Huckel calculation based on occupancy averaged atomic RHF calculations, doi:10.1021/acs.jctc.8b01089
In contrast to init_guess_by_huckel, this routine employs the updated GWH rule from doi:10.1021/ja00480a005 to form the guess.
 Returns:
Density matrix, 2D ndarray
 init_guess_by_sap(mol=None, breaksym=None, **kwargs)[source]#
Generate initial guess density matrix from a superposition of atomic potentials (SAP), doi:10.1021/acs.jctc.8b01089. This is the Gaussian fit implementation, see doi:10.1063/5.0004046.
 Args:
 molMoleBase object
the molecule object for which the initial guess is evaluated
 sap_basisdict
SAP basis in internal format (python dictionary)
 Returns:
 dm0ndarray
SAP initial guess density matrix
 make_asym_dm(mo1, mo2, occ1, occ2, x)[source]#
Oneparticle asymmetric density matrix
 Args:
 mo1, mo22D ndarrays
Molecualr orbital coefficients
 occ1, occ2: 2D ndarrays
Occupation numbers
 x: 2D ndarrays
\(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\). See also
det_ovlp()
 Return:
A list of 2D ndarrays for alpha and beta spin
Examples:
>>> mf1 = scf.UHF(gto.M(atom='H 0 0 0; F 0 0 1.3', basis='ccpvdz')).run() >>> mf2 = scf.UHF(gto.M(atom='H 0 0 0; F 0 0 1.4', basis='ccpvdz')).run() >>> s = gto.intor_cross('int1e_ovlp_sph', mf1.mol, mf2.mol) >>> det, x = det_ovlp(mf1.mo_coeff, mf1.mo_occ, mf2.mo_coeff, mf2.mo_occ, s) >>> adm = make_asym_dm(mf1.mo_coeff, mf1.mo_occ, mf2.mo_coeff, mf2.mo_occ, x) >>> adm.shape (2, 19, 19)
 make_rdm1(mo_coeff=None, mo_occ=None, **kwargs)[source]#
Oneparticle density matrix in AO representation
 Args:
 mo_coefftuple of 2D ndarrays
Orbital coefficients for alpha and beta spins. Each column is one orbital.
 mo_occtuple of 1D ndarrays
Occupancies for alpha and beta spins.
 Returns:
A list of 2D ndarrays for alpha and beta spins
 make_rdm2(mo_coeff=None, mo_occ=None, **kwargs)[source]#
Twoparticle density matrix in AO representation
 Args:
 mo_coefftuple of 2D ndarrays
Orbital coefficients for alpha and beta spins. Each column is one orbital.
 mo_occtuple of 1D ndarrays
Occupancies for alpha and beta spins.
 Returns:
A tuple of three 4D ndarrays for alpha,alpha and alpha,beta and beta,beta spins
 mulliken_meta(mol=None, dm=None, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs. In the metalowdin, the AOs are grouped in three sets: core, valence and Rydberg, the orthogonalization are carried out within each subsets.
 Args:
mol : an instance of
Mole
 dmndarray or 2item list of ndarray
Density matrix. ROHF dm is a 2item list of 2D array
 Kwargs:
verbose : int or instance of
lib.logger.Logger
 pre_orth_methodstr
Preorthogonalization, which localized GTOs for each atom. To obtain the occupied and unoccupied atomic shells, there are three methods
‘ano’ : Project GTOs to ANO basis‘minao’ : Project GTOs to MINAO basis‘scf’ : Symmetryaveraged fractional occupation atomic RHF
 Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 mulliken_pop(mol=None, dm=None, s=None, verbose=5)[source]#
Mulliken population analysis
\[M_{ij} = D_{ij} S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\] Returns:
A list : pop, charges
 popnparray
Mulliken population on each atomic orbitals
 chargesnparray
Mulliken charges
 property nelec#
 property nelectron_alpha#
 spin_square(mo_coeff=None, s=None)[source]#
Spin square and multiplicity of UHF determinant
\[S^2 = \frac{1}{2}(S_+ S_ + S_ S_+) + S_z^2\]where \(S_+ = \sum_i S_{i+}\) is effective for all beta occupied orbitals; \(S_ = \sum_i S_{i}\) is effective for all alpha occupied orbitals.
 There are two possibilities for \(S_+ S_\)
same electron \(S_+ S_ = \sum_i s_{i+} s_{i}\),
\[\sum_i \langle UHFs_{i+} s_{i}UHF\rangle = \sum_{pq}\langle ps_+s_q\rangle \gamma_{qp} = n_\alpha\]2) different electrons \(S_+ S_ = \sum s_{i+} s_{j}, (i\neq j)\). There are in total \(n(n1)\) terms. As a twoparticle operator,
\[\langle S_+ S_ \rangle = \langle ijs_+ s_ij\rangle  \langle ijs_+ s_ji\rangle = \langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle\]
 Similarly, for \(S_ S_+\)
same electron
\[\sum_i \langle s_{i} s_{i+}\rangle = n_\beta\]different electrons
\[\langle S_ S_+ \rangle = \langle i^\betaj^\alpha\rangle \langle j^\alphai^\beta\rangle\]
 For \(S_z^2\)
same electron
\[\langle s_z^2\rangle = \frac{1}{4}(n_\alpha + n_\beta)\]different electrons
\[\begin{split}&\frac{1}{2}\sum_{ij}(\langle ij2s_{z1}s_{z2}ij\rangle \langle ij2s_{z1}s_{z2}ji\rangle) \\ &=\frac{1}{4}(\langle i^\alphai^\alpha\rangle \langle j^\alphaj^\alpha\rangle  \langle i^\alphai^\alpha\rangle \langle j^\betaj^\beta\rangle  \langle i^\betai^\beta\rangle \langle j^\alphaj^\alpha\rangle + \langle i^\betai^\beta\rangle \langle j^\betaj^\beta\rangle) \\ &\frac{1}{4}(\langle i^\alphaj^\alpha\rangle \langle j^\alphai^\alpha\rangle + \langle i^\betaj^\beta\rangle\langle j^\betai^\beta\rangle) \\ &=\frac{1}{4}(n_\alpha^2  n_\alpha n_\beta  n_\beta n_\alpha + n_\beta^2) \frac{1}{4}(n_\alpha + n_\beta) \\ &=\frac{1}{4}((n_\alphan_\beta)^2  (n_\alpha+n_\beta))\end{split}\]
In total
\[\begin{split}\langle S^2\rangle &= \frac{1}{2} (n_\alpha\sum_{ij}\langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle +n_\beta \sum_{ij}\langle i^\betaj^\alpha\rangle\langle j^\alphai^\beta\rangle) + \frac{1}{4}(n_\alphan_\beta)^2 \\\end{split}\] Args:
 moa list of 2 ndarrays
Occupied alpha and occupied beta orbitals
 Kwargs:
 sndarray
AO overlap
 Returns:
A list of two floats. The first is the expectation value of S^2. The second is the corresponding 2S+1
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> mo = (mf.mo_coeff[0][:,mf.mo_occ[0]>0], mf.mo_coeff[1][:,mf.mo_occ[1]>0]) >>> print('S^2 = %.7f, 2S+1 = %.7f' % spin_square(mo, mol.intor('int1e_ovlp_sph'))) S^2 = 0.7570150, 2S+1 = 2.0070027
 stability(internal=True, external=False, verbose=None, return_status=False, **kwargs)[source]#
Stability analysis for UHF/UKS method.
See also pyscf.scf.stability.uhf_stability function.
 Args:
mf : UHF or UKS object
 Kwargs:
 internalbool
Internal stability, within the UHF space.
 externalbool
External stability. Including the UHF > GHF and real > complex stability analysis.
 return_status: bool
Whether to return stable_i and stable_e
 Returns:
If return_status is False (default), the return value includes two set of orbitals, which are more close to the stable condition. The first corresponds to the internal stability and the second corresponds to the external stability.
Else, another two boolean variables (indicating current status: stable or unstable) are returned. The first corresponds to the internal stability and the second corresponds to the external stability.
 to_gpu(out=None)#
Convert a method to its corresponding GPU variant, and recursively converts all attributes of a method to cupy objects or gpu4pyscf objects.
 pyscf.scf.uhf.analyze(mf, verbose=5, with_meta_lowdin=True, **kwargs)[source]#
Analyze the given SCF object: print orbital energies, occupancies; print orbital coefficients; Mulliken population analysis; Dipole moment; Spin density for AOs and atoms;
 pyscf.scf.uhf.canonicalize(mf, mo_coeff, mo_occ, fock=None)[source]#
Canonicalization diagonalizes the UHF Fock matrix within occupied, virtual subspaces separatedly (without change occupancy).
 pyscf.scf.uhf.det_ovlp(mo1, mo2, occ1, occ2, ovlp)[source]#
Calculate the overlap between two different determinants. It is the product of single values of molecular orbital overlap matrix.
\[S_{12} = \langle \Psi_A  \Psi_B \rangle = (\mathrm{det}\mathbf{U}) (\mathrm{det}\mathbf{V^\dagger}) \prod\limits_{i=1}\limits^{2N} \lambda_{ii}\]where \(\mathbf{U}, \mathbf{V}, \lambda\) are unitary matrices and single values generated by single value decomposition(SVD) of the overlap matrix \(\mathbf{O}\) which is the overlap matrix of two sets of molecular orbitals:
\[\mathbf{U}^\dagger \mathbf{O} \mathbf{V} = \mathbf{\Lambda}\] Args:
 mo1, mo22D ndarrays
Molecualr orbital coefficients
 occ1, occ2: 2D ndarrays
occupation numbers
 Return:
 A list:
the product of single values: float (x_a, x_b): 1D ndarrays \(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\) They are used to calculate asymmetric density matrix
 pyscf.scf.uhf.energy_elec(mf, dm=None, h1e=None, vhf=None)[source]#
Electronic energy of Unrestricted HartreeFock
Note this function has side effects which cause mf.scf_summary updated.
 Returns:
HartreeFock electronic energy and the 2electron part contribution
 pyscf.scf.uhf.get_fock(mf, h1e=None, s1e=None, vhf=None, dm=None, cycle=1, diis=None, diis_start_cycle=None, level_shift_factor=None, damp_factor=None, fock_last=None)[source]#
 pyscf.scf.uhf.get_veff(mol, dm, dm_last=0, vhf_last=0, hermi=1, vhfopt=None)[source]#
Unrestricted HartreeFock potential matrix of alpha and beta spins, for the given density matrix
\[\begin{split}V_{ij}^\alpha &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\alpha \\ V_{ij}^\beta &= \sum_{kl} (ijkl)(\gamma_{lk}^\alpha+\gamma_{lk}^\beta)  \sum_{kl} (ilkj)\gamma_{lk}^\beta\end{split}\] Args:
mol : an instance of
Mole
 dma list of ndarrays
A list of density matrices, stored as (alpha,alpha,…,beta,beta,…)
 Kwargs:
 dm_lastndarray or a list of ndarrays or 0
The density matrix baseline. When it is not 0, this function computes the increment of HF potential w.r.t. the reference HF potential matrix.
 vhf_lastndarray or a list of ndarrays or 0
The reference HF potential matrix.
 hermiint
Whether J, K matrix is hermitian
0 : no hermitian or symmetric1 : hermitian2 : antihermitian vhfopt :
A class which holds precomputed quantities to optimize the computation of J, K matrices
 Returns:
\(V_{hf} = (V^\alpha, V^\beta)\). \(V^\alpha\) (and \(V^\beta\)) can be a list matrices, corresponding to the input density matrices.
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1') >>> dmsa = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dmsb = numpy.random.random((3,mol.nao_nr(),mol.nao_nr())) >>> dms = numpy.vstack((dmsa,dmsb)) >>> dms.shape (6, 2, 2) >>> vhfa, vhfb = scf.uhf.get_veff(mol, dms, hermi=0) >>> vhfa.shape (3, 2, 2) >>> vhfb.shape (3, 2, 2)
 pyscf.scf.uhf.init_guess_by_chkfile(mol, chkfile_name, project=None)[source]#
Read SCF chkfile and make the density matrix for UHF initial guess.
 Kwargs:
 projectNone or bool
Whether to project chkfile’s orbitals to the new basis. Note when the geometry of the chkfile and the given molecule are very different, this projection can produce very poor initial guess. In PES scanning, it is recommended to switch off project.
If project is set to None, the projection is only applied when the basis sets of the chkfile’s molecule are different to the basis sets of the given molecule (regardless whether the geometry of the two molecules are different). Note the basis sets are considered to be different if the two molecules are derived from the same molecule with different ordering of atoms.
 pyscf.scf.uhf.init_guess_by_minao(mol, breaksym=None)[source]#
Generate initial guess density matrix based on ANO basis, then project the density matrix to the basis set defined by
mol
 Returns:
Density matrices, a list of 2D ndarrays for alpha and beta spins
 pyscf.scf.uhf.make_asym_dm(mo1, mo2, occ1, occ2, x)[source]#
Oneparticle asymmetric density matrix
 Args:
 mo1, mo22D ndarrays
Molecualr orbital coefficients
 occ1, occ2: 2D ndarrays
Occupation numbers
 x: 2D ndarrays
\(\mathbf{U} \mathbf{\Lambda}^{1} \mathbf{V}^\dagger\). See also
det_ovlp()
 Return:
A list of 2D ndarrays for alpha and beta spin
Examples:
>>> mf1 = scf.UHF(gto.M(atom='H 0 0 0; F 0 0 1.3', basis='ccpvdz')).run() >>> mf2 = scf.UHF(gto.M(atom='H 0 0 0; F 0 0 1.4', basis='ccpvdz')).run() >>> s = gto.intor_cross('int1e_ovlp_sph', mf1.mol, mf2.mol) >>> det, x = det_ovlp(mf1.mo_coeff, mf1.mo_occ, mf2.mo_coeff, mf2.mo_occ, s) >>> adm = make_asym_dm(mf1.mo_coeff, mf1.mo_occ, mf2.mo_coeff, mf2.mo_occ, x) >>> adm.shape (2, 19, 19)
 pyscf.scf.uhf.make_rdm1(mo_coeff, mo_occ, **kwargs)[source]#
Oneparticle density matrix in AO representation
 Args:
 mo_coefftuple of 2D ndarrays
Orbital coefficients for alpha and beta spins. Each column is one orbital.
 mo_occtuple of 1D ndarrays
Occupancies for alpha and beta spins.
 Returns:
A list of 2D ndarrays for alpha and beta spins
 pyscf.scf.uhf.make_rdm2(mo_coeff, mo_occ)[source]#
Twoparticle density matrix in AO representation
 Args:
 mo_coefftuple of 2D ndarrays
Orbital coefficients for alpha and beta spins. Each column is one orbital.
 mo_occtuple of 1D ndarrays
Occupancies for alpha and beta spins.
 Returns:
A tuple of three 4D ndarrays for alpha,alpha and alpha,beta and beta,beta spins
 pyscf.scf.uhf.mulliken_meta(mol, dm_ao, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken population analysis, based on metaLowdin AOs.
 pyscf.scf.uhf.mulliken_meta_spin(mol, dm_ao, verbose=5, pre_orth_method='ANO', s=None)[source]#
Mulliken spin population analysis, based on metaLowdin AOs.
 pyscf.scf.uhf.mulliken_pop_meta_lowdin_ao(mol, dm_ao, verbose=5, pre_orth_method='ANO', s=None)#
Mulliken population analysis, based on metaLowdin AOs.
 pyscf.scf.uhf.mulliken_spin_pop(mol, dm, s=None, verbose=5)[source]#
Mulliken spin density analysis
See Eq. 80 in https://arxiv.org/pdf/1206.2234.pdf and the surrounding text for more details.
\[M_{ij} = (D^a_{ij}  D^b_{ij}) S_{ji}\]Mulliken charges
\[\delta_i = \sum_j M_{ij}\] Returns:
A list : spin_pop, Ms
 spin_popnparray
Mulliken spin density on each atomic orbitals
 Msnparray
Mulliken spin density on each atom
 pyscf.scf.uhf.mulliken_spin_pop_meta_lowdin_ao(mol, dm_ao, verbose=5, pre_orth_method='ANO', s=None)#
Mulliken spin population analysis, based on metaLowdin AOs.
 pyscf.scf.uhf.spin_square(mo, s=1)[source]#
Spin square and multiplicity of UHF determinant
\[S^2 = \frac{1}{2}(S_+ S_ + S_ S_+) + S_z^2\]where \(S_+ = \sum_i S_{i+}\) is effective for all beta occupied orbitals; \(S_ = \sum_i S_{i}\) is effective for all alpha occupied orbitals.
 There are two possibilities for \(S_+ S_\)
same electron \(S_+ S_ = \sum_i s_{i+} s_{i}\),
\[\sum_i \langle UHFs_{i+} s_{i}UHF\rangle = \sum_{pq}\langle ps_+s_q\rangle \gamma_{qp} = n_\alpha\]2) different electrons \(S_+ S_ = \sum s_{i+} s_{j}, (i\neq j)\). There are in total \(n(n1)\) terms. As a twoparticle operator,
\[\langle S_+ S_ \rangle = \langle ijs_+ s_ij\rangle  \langle ijs_+ s_ji\rangle = \langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle\]
 Similarly, for \(S_ S_+\)
same electron
\[\sum_i \langle s_{i} s_{i+}\rangle = n_\beta\]different electrons
\[\langle S_ S_+ \rangle = \langle i^\betaj^\alpha\rangle \langle j^\alphai^\beta\rangle\]
 For \(S_z^2\)
same electron
\[\langle s_z^2\rangle = \frac{1}{4}(n_\alpha + n_\beta)\]different electrons
\[\begin{split}&\frac{1}{2}\sum_{ij}(\langle ij2s_{z1}s_{z2}ij\rangle \langle ij2s_{z1}s_{z2}ji\rangle) \\ &=\frac{1}{4}(\langle i^\alphai^\alpha\rangle \langle j^\alphaj^\alpha\rangle  \langle i^\alphai^\alpha\rangle \langle j^\betaj^\beta\rangle  \langle i^\betai^\beta\rangle \langle j^\alphaj^\alpha\rangle + \langle i^\betai^\beta\rangle \langle j^\betaj^\beta\rangle) \\ &\frac{1}{4}(\langle i^\alphaj^\alpha\rangle \langle j^\alphai^\alpha\rangle + \langle i^\betaj^\beta\rangle\langle j^\betai^\beta\rangle) \\ &=\frac{1}{4}(n_\alpha^2  n_\alpha n_\beta  n_\beta n_\alpha + n_\beta^2) \frac{1}{4}(n_\alpha + n_\beta) \\ &=\frac{1}{4}((n_\alphan_\beta)^2  (n_\alpha+n_\beta))\end{split}\]
In total
\[\begin{split}\langle S^2\rangle &= \frac{1}{2} (n_\alpha\sum_{ij}\langle i^\alphaj^\beta\rangle \langle j^\betai^\alpha\rangle +n_\beta \sum_{ij}\langle i^\betaj^\alpha\rangle\langle j^\alphai^\beta\rangle) + \frac{1}{4}(n_\alphan_\beta)^2 \\\end{split}\] Args:
 moa list of 2 ndarrays
Occupied alpha and occupied beta orbitals
 Kwargs:
 sndarray
AO overlap
 Returns:
A list of two floats. The first is the expectation value of S^2. The second is the corresponding 2S+1
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> mo = (mf.mo_coeff[0][:,mf.mo_occ[0]>0], mf.mo_coeff[1][:,mf.mo_occ[1]>0]) >>> print('S^2 = %.7f, 2S+1 = %.7f' % spin_square(mo, mol.intor('int1e_ovlp_sph'))) S^2 = 0.7570150, 2S+1 = 2.0070027
pyscf.scf.uhf_symm module#
Nonrelativistic unrestricted HartreeFock with point group symmetry.
 class pyscf.scf.uhf_symm.HF1e(mol)[source]#
Bases:
SymAdaptedUHF
 scf(*args)#
SCF main driver
 Kwargs:
 dm0ndarray
If given, it will be used as the initial guess density matrix
Examples:
>>> import numpy >>> from pyscf import gto, scf >>> mol = gto.M(atom='H 0 0 0; F 0 0 1.1') >>> mf = scf.hf.SCF(mol) >>> dm_guess = numpy.eye(mol.nao_nr()) >>> mf.kernel(dm_guess) converged SCF energy = 98.5521904482821 98.552190448282104
 class pyscf.scf.uhf_symm.SymAdaptedUHF(mol)[source]#
Bases:
UHF
SCF base class. nonrelativistic RHF.
 Attributes:
 verboseint
Print level. Default value equals to
Mole.verbose
 max_memoryfloat or int
Allowed memory in MB. Default equals to
Mole.max_memory
 chkfilestr
checkpoint file to save MOs, orbital energies etc. Writing to chkfile can be disabled if this attribute is set to None or False.
 conv_tolfloat
converge threshold. Default is 1e9
 conv_tol_gradfloat
gradients converge threshold. Default is sqrt(conv_tol)
 max_cycleint
max number of iterations. If max_cycle <= 0, SCF iteration will be skipped and the kernel function will compute only the total energy based on the initial guess. Default value is 50.
 init_guessstr
initial guess method. It can be one of ‘minao’, ‘atom’, ‘huckel’, ‘hcore’, ‘1e’, ‘sap’, ‘chkfile’. Default is ‘minao’
 sap_basisstr or dict
basis for SAP initial guess, either filename or path as str or internal format dictionary.
 DIISDIIS class
The class to generate diis object. It can be one of diis.SCF_DIIS, diis.ADIIS, diis.EDIIS.
 diisboolean or object of DIIS class defined in
scf.diis
. Default is the object associated to the attribute
self.DIIS
. Set it to None/False to turn off DIIS. Note if this attribute is initialized as a DIIS object, the SCF driver will use this object in the iteration. The DIIS information (vector basis and error vector) will be held inside this object. When kernel function is called again, the old states (vector basis and error vector) will be reused. diis_spaceint
DIIS space size. By default, 8 Fock matrices and errors vector are stored.
 diis_dampfloat
DIIS damping factor. Default is 0.
 diis_start_cycleint
The step to start DIIS. Default is 1.
 diis_file: ‘str’
File to store DIIS vectors and error vectors.
 level_shiftfloat or int
Level shift (in AU) for virtual space. Default is 0.
 direct_scfbool
Direct SCF is used by default.
 direct_scf_tolfloat
Direct SCF cutoff threshold. Default is 1e13.
 callbackfunction(envs_dict) => None
callback function takes one dict as the argument which is generated by the builtin function
locals()
, so that the callback function can access all local variables in the current environment. conv_checkbool
An extra cycle to check convergence after SCF iterations.
 check_convergencefunction(envs) => bool
A hook for overloading convergence criteria in SCF iterations.
Saved results:
 convergedbool
Whether the SCF iteration converged
 e_totfloat
Total HF energy (electronic energy plus nuclear repulsion)
 mo_energy :
Orbital energies
 mo_occ
Orbital occupancy
 mo_coeff
Orbital coefficients
 cyclesint
The number of iteration cycles performed
Examples:
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1', basis='ccpvdz') >>> mf = scf.hf.SCF(mol) >>> mf.verbose = 0 >>> mf.level_shift = .4 >>> mf.scf() 1.0811707843775884
 Attributes for UHF:
 nelec(int, int)
If given, freeze the number of (alpha,beta) electrons to the given value.
 level_shiftnumber or twoelement list
level shift (in Eh) for alpha and beta Fock if twoelement list is given.
 init_guess_breaksymint
This configuration controls the algorithm used to break the spin symmetry of the initial guess:  0 to disable symmetry breaking in the initial guess.  1 to use the default algorithm introduced in pyscf1.7.  2 to adjust the num. electrons for spinup and spindown density matrices (issue #1839).
Examples:
>>> mol = gto.M(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='ccpvdz', charge=1, spin=1, verbose=0) >>> mf = scf.UHF(mol) >>> mf.kernel() 75.623975516256706 >>> print('S^2 = %.7f, 2S+1 = %.7f' % mf.spin_square()) S^2 = 0.7570150, 2S+1 = 2.0070027
 Attributes for symmetry allowed UHF:
 irrep_nelecdict
Specify the number of alpha/beta electrons for particular irrep {‘ir_name’:(int,int), …}. For the irreps not listed in these dicts, the program will choose the occupancy based on the orbital energies.
Examp