pyscf.df.grad package#

Submodules#

pyscf.df.grad.casdm2_util module#

pyscf.df.grad.casdm2_util.energy_elec_dferi(mc, mo_cas=None, ci=None, dfcasdm2=None, casdm2=None)[source]#

Evaluate E2 = (P|ij) d_Pij / 2, where d_Pij is the DF-2rdm obtained by solve_df_rdm2. For testing purposes. Note that the only index permutation this function understands is (P|ij) = (P|ji) if i and j span the same range of MOs. The caller has to handle everything else, including, for instance, multiplication by 2 if a nonsymmetric slice of the 2RDM is used.

Args:

mc: MC-SCF energy method object

Kwargs:

mo_cas: ndarray, list, or tuple containing active-space MO coefficients: If a tuple of length 2, the same pair of MO sets are assumed to apply to the internally-contracted and externally-contracted indices of the DF-2rdm: (P|Q)d_Qij = (P|kl)d_ijkl -> (P|Q)d_Qij = (P|ij)d_ijij If a tuple of length 4, the 4 MO sets are applied to ijkl above in that order (first two external, last two internal).
ci: ndarray, tuple, or list containing CI coefficients in mo_cas basis.: Not used if dfcasdm2 is provided.
dfcasdm2: ndarray, tuple, or list containing DF-2rdm in mo_cas basis.: Computed by solve_df_rdm2 if omitted.
casdm2: ndarray, tuple, or list containing rdm2 in mo_cas basis.: Computed by mc_or_mc_grad.fcisolver.make_rdm12 (ci,…) if omitted.

Returns:

energy: list: List of energies corresponding to the dfcasdm2s, E = (P|ij) d_Pij / 2 = (P|ij) (P|Q)^-1 (Q|kl) d_ijkl / 2

pyscf.df.grad.casdm2_util.get_int3c_mo(mol, auxmol, mo_coeff, compact=True, max_memory=None)[source]#

Evaluate (P|uv) c_ui c_vj -> (P|ij)

Args:

mol: gto.Mole auxmol: gto.Mole, contains auxbasis mo_coeff: ndarray, list, or tuple containing MO coefficients

if two ndarrays mo_coeff = (mo0, mo1) are provided, mo0 and mo1 are used for the two AO dimensions

Kwargs:

compact: bool: If true, will return only unique ERIs along the two MO dimensions. Does nothing if mo_coeff contains two different sets of orbitals.
max_memory: int: Maximum memory consumption in MB

Returns:

int3c: ndarray of shape (naux, nmo0, nmo1) or (naux, nmo*(nmo+1)//2)

pyscf.df.grad.casdm2_util.gfock_dferi(mc, mo_cas=None, ci=None, dfcasdm2=None, casdm2=None, max_memory=None, ao_basis=True)[source]#

Evaluate F_ij = (P|ik) d_Pjk - this was a giant reinvention of the wheel that didn’t need to happen because with_df._cderi is plenty good enough to calculate gfock. Oh well.

Args:

mc_grad: MC-SCF gradients method object

Kwargs:

mc_cas: ndarray, list, or tuple containing active-space MO coefficients: If a tuple of length 2, the same pair of MO sets are assumed to apply to the internally-contracted and externally-contracted indices of the DF-2rdm: (P|Q)d_Qij = (P|kl)d_ijkl -> (P|Q)d_Qij = (P|ij)d_ijij If a tuple of length 4, the 4 MO sets are applied to ijkl above in that order (first two external, last two internal).
ci: ndarray, tuple, or list containing CI coefficients in mo_cas basis.: Not used if dfcasdm2 is provided.
dfcasdm2: ndarray, tuple, or list containing DF-2rdm in mo_cas basis.: Computed by solve_df_rdm2 if omitted.
casdm2: ndarray, tuple, or list containing rdm2 in mo_cas basis.: Computed by mc_grad.fcisolver.make_rdm12 (ci,…) if omitted.
max_memory: int: Maximum memory usage in MB
ao_basis: bool: If true, return gfock in AO basis

Returns:

gfock: ndarray of shape (nset, nmo[0], nmo[1]) or (nset, nao, nao)

pyscf.df.grad.casdm2_util.grad_elec_auxresponse_dferi(mc_grad, mo_cas=None, ci=None, dfcasdm2=None, casdm2=None, atmlst=None, max_memory=None, dferi=None, incl_2c=True)[source]#

mc_grad: MC-SCF gradients method object

Kwargs:

mc_cas: ndarray, list, or tuple containing active-space MO coefficients: If a tuple of length 2, the same pair of MO sets are assumed to apply to the internally-contracted and externally-contracted indices of the DF-2rdm: (P|Q)d_Qij = (P|kl)d_ijkl -> (P|Q)d_Qij = (P|ij)d_ijij If a tuple of length 4, the 4 MO sets are applied to ijkl above in that order (first two external, last two internal).
ci: ndarray, tuple, or list containing CI coefficients in mo_cas basis.: Not used if dfcasdm2 is provided.
dfcasdm2: ndarray, tuple, or list containing DF-2rdm in mo_cas basis.: Computed by solve_df_rdm2 if omitted.
casdm2: ndarray, tuple, or list containing rdm2 in mo_cas basis.: Computed by mc_grad.fcisolver.make_rdm12 (ci,…) if omitted.
atmlst: list of integers: List of nonfrozen atoms, as in grad_elec functions. Defaults to list (range (mol.natm))
max_memory: int: Maximum memory usage in MB

dferi: ndarray containing g_Pij for optional precalculation incl_2c: bool

If False, omit the terms depending on (P’|Q)

Returns:

dE: list of ndarray of shape (len (atmlst), 3)

pyscf.df.grad.casdm2_util.grad_elec_dferi(mc_grad, mo_cas=None, ci=None, dfcasdm2=None, casdm2=None, atmlst=None, max_memory=None)[source]#

Evaluate the (P|i’j) d_Pij contribution to the electronic gradient, where d_Pij is the DF-2RDM obtained by solve_df_rdm2. The caller must symmetrize (i.e., [(P|i’j) + (P|ij’)] d_Pij / 2) if necessary.

Args:

mc_grad: MC-SCF gradients method object

Kwargs:

mc_cas: ndarray, list, or tuple containing active-space MO coefficients: If a tuple of length 2, the same pair of MO sets are assumed to apply to the internally-contracted and externally-contracted indices of the DF-2rdm: (P|Q)d_Qij = (P|kl)d_ijkl -> (P|Q)d_Qij = (P|ij)d_ijij If a tuple of length 4, the 4 MO sets are applied to ijkl above in that order (first two external, last two internal).
ci: ndarray, tuple, or list containing CI coefficients in mo_cas basis.: Not used if dfcasdm2 is provided.
dfcasdm2: ndarray, tuple, or list containing DF-2rdm in mo_cas basis.: Computed by solve_df_rdm2 if omitted.
casdm2: ndarray, tuple, or list containing rdm2 in mo_cas basis.: Computed by mc_grad.fcisolver.make_rdm12 (ci,…) if omitted.
atmlst: list of integers: List of nonfrozen atoms, as in grad_elec functions. Defaults to list (range (mol.natm))
max_memory: int: Maximum memory usage in MB

Returns:

dE: ndarray of shape (len (dfcasdm2), len (atmlst), 3)

pyscf.df.grad.casdm2_util.solve_df_eri(mc_or_mc_grad, mo_cas=None, compact=True)[source]#: Solve (P|Q) g_Qij = (P|ij) for g_Qij using MOs i,j. I mean this should be a basic function but whatever.

pyscf.df.grad.casdm2_util.solve_df_rdm2(mc_or_mc_grad, mo_cas=None, ci=None, casdm2=None)[source]#

Solve (P|Q)d_Qij = (P|kl)d_ijkl for d_Qij in the MO basis.

Args:

mc_or_mc_grad: DF-MCSCF energy or gradients method object.

Kwargs:

mo_cas: ndarray, tuple, or list containing active mo coefficients.: if two ndarrays mo_cas = (mo0, mo1) are provided, mo0 and mo1 are assumed to correspond to casdm2’s LAST two dimensions in that order, regardless of len (ci) or len (casdm2). (This will facilitate SA-CASSCF gradients at some point. Note the difference from grad_elec_dferi!)
ci: ndarray, tuple, or list containing CI coefficients in mo_cas basis.: Not used if casdm2 is provided.
casdm2: ndarray, tuple, or list containing rdm2 in mo_cas basis.: Computed by mc_or_mc_grad.fcisolver.make_rdm12 (ci,…) if omitted.
compact: bool: If true, tries to return d_Pqr in lower-triangular form if possible

Returns:

dfcasdm2: ndarray or list containing 3-center 2RDM, d_Pqr, where P is: auxbasis index and q, r are mo_cas basis indices.

pyscf.df.grad.casscf module#

CASSCF analytical nuclear gradients

Ref. J. Comput. Chem., 5, 589

MRH: copied from pyscf.grad.casscf.py on 12/07/2019 Contains my modifications for SA-CASSCF gradients 1. Generalized Fock has nonzero i->a and u->a 2. Memory footprint for differentiated eris bugfix

class pyscf.df.grad.casscf.CASSCF_GradScanner(g)[source]#: Bases: GradScanner

pyscf.df.grad.casscf.Grad#: alias of Gradients

class pyscf.df.grad.casscf.Gradients(mc)[source]#

Bases: Gradients

Non-relativistic restricted Hartree-Fock gradients

as_scanner()#

Generating a nuclear gradients scanner/solver (for geometry optimizer).

The returned solver is a function. This function requires one argument “mol” as input and returns energy and first order nuclear derivatives.

The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the nuc-grad object and 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, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1.1', verbose=0)
>>> mc_grad_scanner = mcscf.CASSCF(scf.RHF(mol), 4, 4).nuc_grad_method().as_scanner()
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.1'))
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.5'))

get_jk(mol=None, dm=None, hermi=0)[source]#: J = ((-nabla i) j| kl) D_lk K = ((-nabla i) j| kl) D_jk

grad_elec(mo_coeff=None, ci=None, atmlst=None, verbose=None)#

kernel(mo_coeff=None, ci=None, atmlst=None, verbose=None)[source]#: Kernel function is the main driver of a method. Every method should define the kernel function as the entry of the calculation. Note the return value of kernel function is not strictly defined. It can be anything related to the method (such as the energy, the wave-function, the DFT mesh grids etc.).

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.df.grad.casscf.as_scanner(mcscf_grad)[source]#

Generating a nuclear gradients scanner/solver (for geometry optimizer).

The returned solver is a function. This function requires one argument “mol” as input and returns energy and first order nuclear derivatives.

The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the nuc-grad object and 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, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1.1', verbose=0)
>>> mc_grad_scanner = mcscf.CASSCF(scf.RHF(mol), 4, 4).nuc_grad_method().as_scanner()
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.1'))
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.5'))

pyscf.df.grad.casscf.grad_elec(mc_grad, mo_coeff=None, ci=None, atmlst=None, verbose=None)[source]#

pyscf.df.grad.rhf module#

pyscf.df.grad.rhf.Grad#: alias of Gradients

class pyscf.df.grad.rhf.Gradients(mf)[source]#

Bases: Gradients

Restricted density-fitting Hartree-Fock gradients

auxbasis_response = True#

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.

extra_force(atom_id, envs)[source]#

Hook for extra contributions in analytical gradients.

Contributions like the response of auxiliary basis in density fitting method, the grid response in DFT numerical integration can be put in this function.

get_j(mol=None, dm=None, hermi=0, omega=None)[source]#

get_jk(mol=None, dm=None, hermi=0, with_j=True, with_k=True, omega=None)[source]#: J = ((-nabla i) j| kl) D_lk K = ((-nabla i) j| kl) D_jk

get_k(mol=None, dm=None, hermi=0, omega=None)[source]#

get_veff(mol=None, dm=None)[source]#

pyscf.df.grad.rhf.get_j(mf_grad, mol=None, dm=None, hermi=0)[source]#

pyscf.df.grad.rhf.get_jk(mf_grad, mol=None, dm=None, hermi=0, with_j=True, with_k=True, decompose_j2c='CD', lindep=1e-07)[source]#

Computes J and K derivatives with density fitting

Args:

mf_grad : instance of Gradients

mol : instance of gto.Mole

dm: numpy.ndarray: Zeroth order density matrix
hermi: int: Is the density matrix hermitian or not
with_j: boolean: Whether to compute J matrix
with_k: boolean: Whether to compute K matrix
decompose_j2c: string: The method to decompose the metric defined by int2c. It can be set to CD (cholesky decomposition) or ED (eigenvalue decomposition).
lindepfloat: The threshold to discard linearly dependent basis when decompose_j2c is set to ED.

pyscf.df.grad.rks module#

pyscf.df.grad.rks.Grad#: alias of Gradients

class pyscf.df.grad.rks.Gradients(mf)[source]#

Bases: Gradients

auxbasis_response = True#

extra_force(atom_id, envs)[source]#

Hook for extra contributions in analytical gradients.

Contributions like the response of auxiliary basis in density fitting method, the grid response in DFT numerical integration can be put in this function.

get_j(mol=None, dm=None, hermi=0, omega=None)[source]#

get_jk(mol=None, dm=None, hermi=0, with_j=True, with_k=True, omega=None)[source]#: J = ((-nabla i) j| kl) D_lk K = ((-nabla i) j| kl) D_jk

get_k(mol=None, dm=None, hermi=0, omega=None)[source]#

get_veff(mol=None, dm=None)#: Coulomb + XC functional

pyscf.df.grad.rks.get_veff(ks_grad, mol=None, dm=None)[source]#: Coulomb + XC functional

pyscf.df.grad.rohf module#

pyscf.df.grad.rohf.Grad#: alias of Gradients

class pyscf.df.grad.rohf.Gradients(mf)[source]#

Bases: Gradients

make_rdm1e(mo_energy, mo_coeff, mo_occ)#: Energy weighted density matrix

pyscf.df.grad.roks module#

pyscf.df.grad.roks.Grad#: alias of Gradients

class pyscf.df.grad.roks.Gradients(mf)[source]#

Bases: Gradients

make_rdm1e(mo_energy, mo_coeff, mo_occ)#: Energy weighted density matrix

pyscf.df.grad.sacasscf module#

class pyscf.df.grad.sacasscf.CASSCF_GradScanner(g, state)[source]#: Bases: GradScanner

class pyscf.df.grad.sacasscf.Gradients(mc, state=None)[source]#

Bases: Gradients

get_LdotJnuc(Lvec, **kwargs)[source]#: Return Lvec . J_nuc, where J_nuc is the Jacobian of the Lagrange cofactors wrt nuclear displacement. This is the second term of the final gradient expectation value.

kernel(**kwargs)[source]#: Kernel function is the main driver of a method. Every method should define the kernel function as the entry of the calculation. Note the return value of kernel function is not strictly defined. It can be anything related to the method (such as the energy, the wave-function, the DFT mesh grids etc.).

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.df.grad.sacasscf.Lci_dot_dgci_dx(Lci, weights, mc, mo_coeff=None, ci=None, atmlst=None, mf_grad=None, eris=None, verbose=None, auxbasis_response=True)[source]#: Modification of pyscf.grad.casscf.kernel to compute instead the CI Lagrange term nuclear gradient (sum_IJ Lci_IJ d2_Ecas/d_lambda d_PIJ) This involves removing all core-core and nuclear-nuclear terms and making the substitution sum_I w_I<L_I|p’q|I> + c.c. -> <0|p’q|0> sum_I w_I<L_I|p’r’sq|I> + c.c. -> <0|p’r’sq|0> The active-core terms (sum_I w_I<L_I|x’iyi|I>, sum_I w_I <L_I|x’iiy|I>, c.c.) must be retained.

pyscf.df.grad.sacasscf.Lorb_dot_dgorb_dx(Lorb, mc, mo_coeff=None, ci=None, atmlst=None, mf_grad=None, eris=None, verbose=None, auxbasis_response=True)[source]#: Modification of pyscf.grad.casscf.kernel to compute instead the orbital Lagrange term nuclear gradient (sum_pq Lorb_pq d2_Ecas/d_lambda d_kpq) This involves removing nuclear-nuclear terms and making the substitution (D_[p]q + D_p[q]) -> D_pq (d_[p]qrs + d_pq[r]s + d_p[q]rs + d_pqr[s]) -> d_pqrs Where [] around an index implies contraction with Lorb from the left, so that the external index (regardless of whether the index on the rdm is bra or ket) is always the first index of Lorb.

pyscf.df.grad.sacasscf.as_scanner(mcscf_grad, state=None)[source]#

Generating a nuclear gradients scanner/solver (for geometry optimizer).

The returned solver is a function. This function requires one argument “mol” as input and returns energy and first order nuclear derivatives.

The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the nuc-grad object and 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, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1.1', verbose=0)
>>> mc_grad_scanner = mcscf.CASSCF(scf.RHF(mol), 4, 4).nuc_grad_method().as_scanner()
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.1'))
>>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.5'))

pyscf.df.grad.uhf module#

pyscf.df.grad.uhf.Grad#: alias of Gradients

class pyscf.df.grad.uhf.Gradients(mf)[source]#

Bases: Gradients

Unrestricted density-fitting Hartree-Fock gradients

auxbasis_response = True#

extra_force(atom_id, envs)[source]#

Hook for extra contributions in analytical gradients.

Contributions like the response of auxiliary basis in density fitting method, the grid response in DFT numerical integration can be put in this function.

get_j(mol=None, dm=None, hermi=0, omega=None)[source]#

get_jk(mol=None, dm=None, hermi=0, with_j=True, with_k=True, omega=None)[source]#: J = ((-nabla i) j| kl) D_lk K = ((-nabla i) j| kl) D_jk

get_k(mol=None, dm=None, hermi=0, omega=None)[source]#

get_veff(mol=None, dm=None)[source]#

pyscf.df.grad.uks module#

pyscf.df.grad.uks.Grad#: alias of Gradients

class pyscf.df.grad.uks.Gradients(mf)[source]#

Bases: Gradients

auxbasis_response = True#

extra_force(atom_id, envs)[source]#

Hook for extra contributions in analytical gradients.

Contributions like the response of auxiliary basis in density fitting method, the grid response in DFT numerical integration can be put in this function.

get_j(mol=None, dm=None, hermi=0, omega=None)[source]#

get_jk(mol=None, dm=None, hermi=0, with_j=True, with_k=True, omega=None)[source]#: J = ((-nabla i) j| kl) D_lk K = ((-nabla i) j| kl) D_jk

get_k(mol=None, dm=None, hermi=0, omega=None)[source]#

get_veff(mol=None, dm=None)#: Coulomb + XC functional

pyscf.df.grad.uks.get_veff(ks_grad, mol=None, dm=None)[source]#: Coulomb + XC functional

pyscf.df.grad package#

Submodules#

pyscf.df.grad.casdm2_util module#

pyscf.df.grad.casscf module#

pyscf.df.grad.rhf module#

pyscf.df.grad.rks module#

pyscf.df.grad.rohf module#

pyscf.df.grad.roks module#

pyscf.df.grad.sacasscf module#

pyscf.df.grad.uhf module#

pyscf.df.grad.uks module#

Module contents#