pyscf.tdscf package#

Submodules#

pyscf.tdscf.dhf module#

TDA and TDHF for no-pair DKS Hamiltonian

class pyscf.tdscf.dhf.TDA(mf)[source]#

Bases: TDBase

Tamm-Dancoff approximation

Attributes:

conv_tolfloat: Diagonalization convergence tolerance. Default is 1e-9.
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 de-excitation 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.

gen_vind(mf=None)[source]#: Generate function to compute Ax

init_guess(mf, nstates=None, wfnsym=None)[source]#

kernel(x0=None, nstates=None)[source]#: TDA diagonalization solver

singlet = None#

class pyscf.tdscf.dhf.TDBase(mf)[source]#

Bases: TDBase

analyze(verbose=None)#

get_ab(mf=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

get_nto(state=1, threshold=0.3, verbose=None)#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

tdobj : TDA, or TDHF, or TDDFT object

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

nuc_grad_method()[source]#

class pyscf.tdscf.dhf.TDHF(mf)[source]#

Bases: TDBase

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

get_precond(hdiag)#

init_guess(mf, nstates=None, wfnsym=None)[source]#

kernel(x0=None, nstates=None)[source]#: TDHF diagonalization with non-Hermitian eigenvalue solver

singlet = None#

pyscf.tdscf.dhf.analyze(tdobj, verbose=None)[source]#

pyscf.tdscf.dhf.gen_tda_hop(mf, fock_ao=None)#: A x

pyscf.tdscf.dhf.gen_tda_operation(mf, fock_ao=None)[source]#: A x

pyscf.tdscf.dhf.gen_tdhf_operation(mf, fock_ao=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

pyscf.tdscf.dhf.get_ab(mf, mo_energy=None, mo_coeff=None, mo_occ=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

pyscf.tdscf.dhf.get_nto(tdobj, state=1, threshold=0.3, verbose=None)[source]#

pyscf.tdscf.dks module#

pyscf.tdscf.dks.RPA#: alias of TDDFT

class pyscf.tdscf.dks.TDA(mf)[source]#: Bases: TDA

class pyscf.tdscf.dks.TDDFT(mf)[source]#: Bases: TDHF

pyscf.tdscf.dks.TDDKS#: alias of TDDFT

pyscf.tdscf.ghf module#

pyscf.tdscf.ghf.CIS#: alias of TDA

pyscf.tdscf.ghf.RPA#: alias of TDHF

class pyscf.tdscf.ghf.TDA(mf)[source]#

Bases: TDBase

Tamm-Dancoff approximation

Attributes:

conv_tolfloat: Diagonalization convergence tolerance. Default is 1e-9.
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 de-excitation 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.

gen_vind(mf=None)[source]#: Generate function to compute Ax

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

kernel(x0=None, nstates=None)[source]#: TDA diagonalization solver

singlet = None#

class pyscf.tdscf.ghf.TDBase(mf)[source]#

Bases: TDBase

analyze(verbose=None)#

get_ab(mf=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

get_nto(state=1, threshold=0.3, verbose=None)#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

tdobj : TDA, or TDHF, or TDDFT object

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

nuc_grad_method()[source]#

pyscf.tdscf.ghf.TDGHF#: alias of TDHF

class pyscf.tdscf.ghf.TDHF(mf)[source]#

Bases: TDBase

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

kernel(x0=None, nstates=None)[source]#: TDHF diagonalization with non-Hermitian eigenvalue solver

singlet = None#

pyscf.tdscf.ghf.analyze(tdobj, verbose=None)[source]#

pyscf.tdscf.ghf.gen_tda_hop(mf, fock_ao=None, wfnsym=None)#

A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.ghf.gen_tda_operation(mf, fock_ao=None, wfnsym=None)[source]#

A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.ghf.gen_tdhf_operation(mf, fock_ao=None, wfnsym=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

pyscf.tdscf.ghf.get_ab(mf, mo_energy=None, mo_coeff=None, mo_occ=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

pyscf.tdscf.ghf.get_nto(tdobj, state=1, threshold=0.3, verbose=None)[source]#

pyscf.tdscf.gks module#

class pyscf.tdscf.gks.CasidaTDDFT(mf)[source]#

Bases: TDDFT, TDA

Solve the Casida TDDFT formula (A-B)(A+B)(X+Y) = (X+Y)w^2

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)#

kernel(x0=None, nstates=None)[source]#: TDDFT diagonalization solver

nuc_grad_method()[source]#

pyscf.tdscf.gks.RPA#: alias of TDDFT

class pyscf.tdscf.gks.TDA(mf)[source]#: Bases: TDA

class pyscf.tdscf.gks.TDDFT(mf)[source]#: Bases: TDHF

pyscf.tdscf.gks.TDDFTNoHybrid#: alias of CasidaTDDFT

pyscf.tdscf.gks.TDGKS#: alias of TDDFT

pyscf.tdscf.gks.tddft(mf)[source]#: Driver to create TDDFT or CasidaTDDFT object

pyscf.tdscf.rhf module#

pyscf.tdscf.rhf.CIS#: alias of TDA

pyscf.tdscf.rhf.RPA#: alias of TDHF

class pyscf.tdscf.rhf.TDA(mf)[source]#

Bases: TDBase

Tamm-Dancoff approximation

Attributes:

conv_tolfloat: Diagonalization convergence tolerance. Default is 1e-9.
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 de-excitation 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.

Gradients(*args, **kwargs)#

gen_vind(mf=None)[source]#: Generate function to compute Ax

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

Generate initial guess for TDA

Kwargs:

nstatesint: The number of initial guess vectors.
wfnsymint or str: The irrep label or ID of the wavefunction.
return_symmetrybool: Whether to return symmetry labels for initial guess vectors.

kernel(x0=None, nstates=None)[source]#: TDA diagonalization solver

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.tdscf.rhf.TDBase(mf)[source]#

Bases: StreamObject

DDCOSMO(solvent_obj=None, dm=None)#

Add solvent model in TDDFT calculations.

Kwargs:

dmif given, solvent does not respond to the change of density: matrix. A frozen ddCOSMO potential is added to the results.

DDPCM(solvent_obj=None, dm=None)#

Add solvent model in TDDFT calculations.

Kwargs:

dmif given, solvent does not respond to the change of density: matrix. A frozen ddCOSMO potential is added to the results.

PCM(solvent_obj=None, dm=None)#

Add solvent model in TDDFT calculations.

Kwargs:

dmif given, solvent does not respond to the change of density: matrix. A frozen ddCOSMO potential is added to the results.

analyze(verbose=None)#

as_scanner()#

Generating a scanner/solver for TDA/TDHF/TDDFT PES.

The returned solver is a function. This function requires one argument “mol” as input and returns total TDA/TDHF/TDDFT energy.

The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the TDA/TDDFT and the underlying SCF objects (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, tdscf
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1')
>>> td_scanner = tdscf.TDHF(scf.RHF(mol)).as_scanner()
>>> de = td_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1'))
[ 0.34460866  0.34460866  0.7131453 ]
>>> de = td_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))
[ 0.14844013  0.14844013  0.47641829]

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_tol = 1e-05#

ddCOSMO(solvent_obj=None, dm=None)#

Add solvent model in TDDFT calculations.

Kwargs:

dmif given, solvent does not respond to the change of density: matrix. A frozen ddCOSMO potential is added to the results.

ddPCM(solvent_obj=None, dm=None)#

Add solvent model in TDDFT calculations.

Kwargs:

dmif given, solvent does not respond to the change of density: matrix. A frozen ddCOSMO potential is added to the results.

deg_eia_thresh = 0.001#

dump_flags(verbose=None)[source]#

property e_tot#: Excited state energies

gen_vind(mf=None)[source]#

get_ab(mf=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

Ref: Chem Phys Lett, 256, 454

get_nto(state=1, threshold=0.3, verbose=None)#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

tdobj : TDA, or TDHF, or TDDFT object

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

get_precond(hdiag)[source]#

level_shift = 0#

lindep = 1e-12#

max_cycle = 100#

property nroots#

nstates = 3#

nuc_grad_method()[source]#

oscillator_strength(e=None, xy=None, gauge='length', order=0)#

positive_eig_threshold = 0.001#

reset(mol=None)[source]#

singlet = True#

to_gpu()[source]#

transition_dipole(xy=None)#: Transition dipole moments in the length gauge

transition_magnetic_dipole(xy=None)#: Transition magnetic dipole moments (imaginary part only)

transition_magnetic_quadrupole(xy=None)#: Transition magnetic quadrupole moments (imaginary part only)

transition_octupole(xy=None)#: Transition octupole moments in the length gauge

transition_quadrupole(xy=None)#: Transition quadrupole moments in the length gauge

transition_velocity_dipole(xy=None)#: Transition dipole moments in the velocity gauge (imaginary part only)

transition_velocity_octupole(xy=None)#: Transition octupole moments in the velocity gauge (imaginary part only)

transition_velocity_quadrupole(xy=None)#: Transition quadrupole moments in the velocity gauge (imaginary part only)

class pyscf.tdscf.rhf.TDHF(mf)[source]#

Bases: TDBase

Time-dependent Hartree-Fock

Attributes:

conv_tolfloat: Diagonalization convergence tolerance. Default is 1e-4.
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 de-excitation 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.

Gradients(*args, **kwargs)#

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

kernel(x0=None, nstates=None)[source]#: TDHF diagonalization with non-Hermitian eigenvalue solver

nuc_grad_method()[source]#

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.tdscf.rhf.TDRHF#: alias of TDHF

class pyscf.tdscf.rhf.TD_Scanner(td)[source]#: Bases: SinglePointScanner

pyscf.tdscf.rhf.analyze(tdobj, verbose=None)[source]#

pyscf.tdscf.rhf.as_scanner(td)[source]#

Generating a scanner/solver for TDA/TDHF/TDDFT PES.

The returned solver is a function. This function requires one argument “mol” as input and returns total TDA/TDHF/TDDFT energy.

The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the TDA/TDDFT and the underlying SCF objects (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, tdscf
>>> mol = gto.M(atom='H 0 0 0; F 0 0 1')
>>> td_scanner = tdscf.TDHF(scf.RHF(mol)).as_scanner()
>>> de = td_scanner(gto.M(atom='H 0 0 0; F 0 0 1.1'))
[ 0.34460866  0.34460866  0.7131453 ]
>>> de = td_scanner(gto.M(atom='H 0 0 0; F 0 0 1.5'))
[ 0.14844013  0.14844013  0.47641829]

pyscf.tdscf.rhf.gen_tda_hop(mf, fock_ao=None, singlet=True, wfnsym=None)#

Generate function to compute A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.rhf.gen_tda_operation(mf, fock_ao=None, singlet=True, wfnsym=None)[source]#

Generate function to compute A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.rhf.gen_tdhf_operation(mf, fock_ao=None, singlet=True, wfnsym=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

pyscf.tdscf.rhf.get_ab(mf, mo_energy=None, mo_coeff=None, mo_occ=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

Ref: Chem Phys Lett, 256, 454

pyscf.tdscf.rhf.get_nto(tdobj, state=1, threshold=0.3, verbose=None)[source]#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

tdobj : TDA, or TDHF, or TDDFT object

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

pyscf.tdscf.rhf.oscillator_strength(tdobj, e=None, xy=None, gauge='length', order=0)[source]#

pyscf.tdscf.rhf.transition_dipole(tdobj, xy=None)[source]#: Transition dipole moments in the length gauge

pyscf.tdscf.rhf.transition_magnetic_dipole(tdobj, xy=None)[source]#: Transition magnetic dipole moments (imaginary part only)

pyscf.tdscf.rhf.transition_magnetic_quadrupole(tdobj, xy=None)[source]#: Transition magnetic quadrupole moments (imaginary part only)

pyscf.tdscf.rhf.transition_octupole(tdobj, xy=None)[source]#: Transition octupole moments in the length gauge

pyscf.tdscf.rhf.transition_quadrupole(tdobj, xy=None)[source]#: Transition quadrupole moments in the length gauge

pyscf.tdscf.rhf.transition_velocity_dipole(tdobj, xy=None)[source]#: Transition dipole moments in the velocity gauge (imaginary part only)

pyscf.tdscf.rhf.transition_velocity_octupole(tdobj, xy=None)[source]#: Transition octupole moments in the velocity gauge (imaginary part only)

pyscf.tdscf.rhf.transition_velocity_quadrupole(tdobj, xy=None)[source]#: Transition quadrupole moments in the velocity gauge (imaginary part only)

pyscf.tdscf.rks module#

class pyscf.tdscf.rks.CasidaTDDFT(mf)[source]#

Bases: TDDFT, TDA

Solve the Casida TDDFT formula (A-B)(A+B)(X+Y) = (X+Y)w^2

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

get_precond(hdiag)#

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)#

Generate initial guess for TDA

Kwargs:

nstatesint: The number of initial guess vectors.
wfnsymint or str: The irrep label or ID of the wavefunction.
return_symmetrybool: Whether to return symmetry labels for initial guess vectors.

kernel(x0=None, nstates=None)[source]#: TDDFT diagonalization solver

nuc_grad_method()[source]#

pyscf.tdscf.rks.RPA#: alias of TDDFT

class pyscf.tdscf.rks.TDA(mf)[source]#

Bases: TDA

Gradients(*args, **kwargs)#

nuc_grad_method()[source]#

class pyscf.tdscf.rks.TDDFT(mf)[source]#

Bases: TDHF

Gradients(*args, **kwargs)#

nuc_grad_method()[source]#

pyscf.tdscf.rks.TDDFTNoHybrid#: alias of CasidaTDDFT

pyscf.tdscf.rks.TDH#: alias of dRPA

pyscf.tdscf.rks.TDRKS#: alias of TDDFT

class pyscf.tdscf.rks.dRPA(mf)[source]#: Bases: CasidaTDDFT

class pyscf.tdscf.rks.dTDA(mf)[source]#: Bases: TDA

pyscf.tdscf.rks.tddft(mf)[source]#: Driver to create TDDFT or CasidaTDDFT object

pyscf.tdscf.uhf module#

pyscf.tdscf.uhf.CIS#: alias of TDA

pyscf.tdscf.uhf.RPA#: alias of TDHF

class pyscf.tdscf.uhf.TDA(mf)[source]#

Bases: TDBase

Tamm-Dancoff approximation

Attributes:

conv_tolfloat: Diagonalization convergence tolerance. Default is 1e-9.
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 de-excitation 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.

Gradients(*args, **kwargs)#

gen_vind(mf=None)[source]#: Generate function to compute Ax

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

kernel(x0=None, nstates=None)[source]#: TDA diagonalization solver

singlet = None#

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.tdscf.uhf.TDBase(mf)[source]#

Bases: TDBase

analyze(verbose=None)#

get_ab(mf=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

Spin symmetry is considered in the returned A, B lists. List A has three items: (A_aaaa, A_aabb, A_bbbb). A_bbaa = A_aabb.transpose(2,3,0,1). B has three items: (B_aaaa, B_aabb, B_bbbb). B_bbaa = B_aabb.transpose(2,3,0,1).

get_nto(state=1, threshold=0.3, verbose=None)#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

nuc_grad_method()[source]#

class pyscf.tdscf.uhf.TDHF(mf)[source]#

Bases: TDBase

Gradients(*args, **kwargs)#

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)[source]#

kernel(x0=None, nstates=None)[source]#: TDHF diagonalization with non-Hermitian eigenvalue solver

singlet = None#

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.tdscf.uhf.TDUHF#: alias of TDHF

pyscf.tdscf.uhf.analyze(tdobj, verbose=None)[source]#

pyscf.tdscf.uhf.gen_tda_hop(mf, fock_ao=None, wfnsym=None)#

A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.uhf.gen_tda_operation(mf, fock_ao=None, wfnsym=None)[source]#

A x

Kwargs:

wfnsymint or str: Point group symmetry irrep symbol or ID for excited CIS wavefunction.

pyscf.tdscf.uhf.gen_tdhf_operation(mf, fock_ao=None, singlet=True, wfnsym=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

pyscf.tdscf.uhf.get_ab(mf, mo_energy=None, mo_coeff=None, mo_occ=None)[source]#

A and B matrices for TDDFT response function.

A[i,a,j,b] = delta_{ab}delta_{ij}(E_a - E_i) + (ai||jb) B[i,a,j,b] = (ai||bj)

Spin symmetry is considered in the returned A, B lists. List A has three items: (A_aaaa, A_aabb, A_bbbb). A_bbaa = A_aabb.transpose(2,3,0,1). B has three items: (B_aaaa, B_aabb, B_bbbb). B_bbaa = B_aabb.transpose(2,3,0,1).

pyscf.tdscf.uhf.get_nto(tdobj, state=1, threshold=0.3, verbose=None)[source]#

Natural transition orbital analysis.

The natural transition density matrix between ground state and excited state \(Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle\) can be transformed to diagonal form through SVD \(T = O \sqrt{\lambda} V^\dagger\). O and V are occupied and virtual natural transition orbitals. The diagonal elements \(\lambda\) are the weights of the occupied-virtual orbital pair in the excitation.

Ref: Martin, R. L., JCP, 118, 4775-4777

Note in the TDHF/TDDFT calculations, the excitation part (X) is interpreted as the CIS coefficients and normalized to 1. The de-excitation part (Y) is ignored.

Args:

stateint: Excited state ID. state = 1 means the first excited state. If state < 0, state ID is counted from the last excited state.

Kwargs:

thresholdfloat: Above which the NTO coefficients will be printed in the output.

Returns:

A list (weights, NTOs). NTOs are natural orbitals represented in AO basis. The first N_occ NTOs are occupied NTOs and the rest are virtual NTOs.

pyscf.tdscf.uks module#

class pyscf.tdscf.uks.CasidaTDDFT(mf)[source]#

Bases: TDDFT, TDA

Solve the Casida TDDFT formula (A-B)(A+B)(X+Y) = (X+Y)w^2

gen_vind(mf=None)[source]#

Generate function to compute

[ A B ][X] [-B* -A*][Y]

get_precond(hdiag)#

init_guess(mf, nstates=None, wfnsym=None, return_symmetry=False)#

kernel(x0=None, nstates=None)[source]#: TDDFT diagonalization solver

nuc_grad_method()[source]#

pyscf.tdscf.uks.RPA#: alias of TDDFT

class pyscf.tdscf.uks.TDA(mf)[source]#

Bases: TDA

Gradients(*args, **kwargs)#

nuc_grad_method()[source]#

class pyscf.tdscf.uks.TDDFT(mf)[source]#

Bases: TDHF

Gradients(*args, **kwargs)#

nuc_grad_method()[source]#

pyscf.tdscf.uks.TDDFTNoHybrid#: alias of CasidaTDDFT

pyscf.tdscf.uks.TDH#: alias of dRPA

pyscf.tdscf.uks.TDUKS#: alias of TDDFT

class pyscf.tdscf.uks.dRPA(mf)[source]#: Bases: CasidaTDDFT

class pyscf.tdscf.uks.dTDA(mf)[source]#: Bases: TDA

pyscf.tdscf.uks.tddft(mf)[source]#: Driver to create TDDFT or CasidaTDDFT object

Module contents#

pyscf.tdscf.RPA(mf)[source]#

pyscf.tdscf.TD(mf)#

pyscf.tdscf.TDA(mf)[source]#

pyscf.tdscf.TDDFT(mf)[source]#

pyscf.tdscf.TDHF(mf)[source]#

pyscf.tdscf.dRPA(mf)[source]#

pyscf.tdscf.dTDA(mf)[source]#