PySCF Support

PySCF Support#

Compatibility layer for supporting DFT-D3 in pyscf.

class dftd3.pyscf.DFTD3Dispersion(*args: Any, **kwargs: Any)[source]#

Implementation of the interface for using DFT-D3 in pyscf. The xc functional can be provided in the constructor together with the version of the DFT-D3 damping function to use. Possible damping functions are

"d3bj": (default): For rational damping function
"d3zero": For zero damping function
"d3mbj": Modified damping parameters for the rational damping function
"d3mzero": Modified version of the zero damping function
"d3op": Optimized power damping function

Custom parameters can be provided with the param dictionary. The param dict contains the damping parameters, at least s8, a1 and a2 must be provided for rational damping, while s8 and rs6 are required in case of zero damping.

Parameters for (modified) rational damping are:

Tweakable parameter	Default	Description
s6	1.0	Scaling of the dipole-dipole dispersion
s8	None	Scaling of the dipole-quadrupole dispersion
s9	1.0	Scaling of the three-body dispersion energy
a1	None	Scaling of the critical radii
a2	None	Offset of the critical radii
alp	14.0	Exponent of the zero damping (ATM only)

Parameters for (modified) zero damping are:

Tweakable parameter	Default	Description
s6	1.0	Scaling of the dipole-dipole dispersion
s8	None	Scaling of the dipole-quadrupole dispersion
s9	1.0	Scaling of the three-body dispersion energy
rs6	None	Scaling of the dipole-dipole damping
rs8	1.0	Scaling of the dipole-quadrupole damping
alp	14.0	Exponent of the zero damping
bet	None	Offset for damping radius (modified zero damping)

Parameters for optimized power damping are:

Tweakable parameter	Default	Description
s6	1.0	Scaling of the dipole-dipole dispersion
s8	None	Scaling of the dipole-quadrupole dispersion
s9	1.0	Scaling of the three-body dispersion energy
a1	None	Scaling of the critical radii
a2	None	Offset of the critical radii
alp	14.0	Exponent of the zero damping (ATM only)
bet	None	Power for the zero-damping component

The version of the damping can be changed after constructing the dispersion correction. With the atm boolean the three-body dispersion energy can be enabled, which is generally recommended.

Examples

>>> from pyscf import gto
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          C   -0.189833176  -0.645396435   0.069807761
...          C    1.121636324  -0.354065576   0.439096514
...          C    1.486520953   0.962572632   0.712107225
...          C    0.549329390   1.989209324   0.617868956
...          C   -0.757627135   1.681862630   0.246856908
...          C   -1.138190460   0.370551816  -0.028582325
...          Br  -2.038462778   3.070459841   0.115165429
...          H    1.852935245  -1.146434699   0.514119204
...          H    0.825048723   3.012176989   0.829385472
...          H    2.502259769   1.196433556   1.000317333
...          H   -2.157140187   0.151608161  -0.313181471
...          H   -0.480820487  -1.664983631  -0.142918416
...          S   -4.157443472   5.729584377  -0.878761129
...          H   -4.823791426   4.796089466  -1.563433338
...          C   -2.828338520   5.970593053  -2.091189515
...          H   -2.167577293   6.722356639  -1.668621815
...          H   -2.264954814   5.054835899  -2.240198499
...          H   -3.218524904   6.337447714  -3.035087058
...          '''
... )
>>> d3 = disp.DFTD3Dispersion(mol, xc="PW6B95", version="d3bj")
>>> d3.kernel()[0]
array(-0.01009386)
>>> d3.version = "d3zero"  # Change to zero damping
>>> d3.kernel()[0]
array(-0.00574098)
>>> d3.atm = True  # Activate three-body dispersion
>>> d3.kernel()[0]
array(-0.00574289)

dump_flags(verbose: bool | None = None)[source]#: Show options used for the DFT-D3 dispersion correction.

kernel() → Tuple[float, numpy.ndarray][source]#

Compute the DFT-D3 dispersion correction.

The dispersion model as well as the parameters are created locally and not part of the state of the instance.

Returns:: The energy and gradient of the DFT-D4 dispersion correction.
Return type:: float, ndarray

Examples

>>> from pyscf import gto
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          Br    0.000000    0.000000    1.919978
...          Br    0.000000    0.000000   -0.367147
...          N     0.000000    0.000000   -3.235006
...          C     0.000000    0.000000   -4.376626
...          H     0.000000    0.000000   -5.444276
...          '''
... )
>>> d4 = disp.DFTD3Dispersion(mol, xc="PBE0")
>>> energy, gradient = d4.kernel()
>>> energy
array(-0.00303589)
>>> gradient
array([[ 0.00000000e+00,  0.00000000e+00,  9.66197638e-05],
       [ 0.00000000e+00,  0.00000000e+00,  2.36000434e-04],
       [ 0.00000000e+00,  0.00000000e+00, -1.16718302e-04],
       [ 0.00000000e+00,  0.00000000e+00, -1.84332770e-04],
       [ 0.00000000e+00,  0.00000000e+00, -3.15691249e-05]])

reset(mol: pyscf.gto.Mole)[source]#: Reset mol and clean up relevant attributes for scanner mode

dftd3.pyscf.d3_energy(mf: pyscf.scf.hf.SCF, **kwargs) → pyscf.scf.hf.SCF[source]#

Apply DFT-D3 corrections to SCF or MCSCF methods by returning an instance of a new class built from the original instances class. The dispersion correction is stored in the with_dftd3 attribute of the class.

Parameters:

mf (scf.hf.SCF) – The method to which DFT-D3 corrections will be applied.
**kwargs – Keyword arguments passed to the DFTD3Dispersion class.

Return type:

The method with DFT-D3 corrections applied.

Examples

>>> from pyscf import gto, scf
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          N  -1.57871857  -0.04661102   0.00000000
...          N   1.57871857   0.04661102   0.00000000
...          H  -2.15862174   0.13639605   0.80956529
...          H  -0.84947130   0.65819321   0.00000000
...          H  -2.15862174   0.13639605  -0.80956529
...          H   2.15862174  -0.13639605  -0.80956529
...          H   0.84947130  -0.65819321   0.00000000
...          H   2.15862174  -0.13639605   0.80956529
...          '''
... )
>>> mf = disp.d3_energy(scf.RHF(mol)).run()
converged SCF energy = -110.932603617026
>>> mf.kernel()
-110.93260361702605

dftd3.pyscf.d3_grad(scf_grad: pyscf.grad.rhf.GradientsBase, **kwargs)[source]#

Apply DFT-D3 corrections to SCF or MCSCF nuclear gradients methods by returning an instance of a new class built from the original class. The dispersion correction is stored in the with_dftd3 attribute of the class.

Parameters:

scf_grad (rhf_grad.Gradients) – The method to which DFT-D3 corrections will be applied.
**kwargs – Keyword arguments passed to the DFTD3Dispersion class.

Return type:

The method with DFT-D3 corrections applied.

Examples

>>> from pyscf import gto, scf
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          O  -1.65542061  -0.12330038   0.00000000
...          O   1.24621244   0.10268870   0.00000000
...          H  -0.70409026   0.03193167   0.00000000
...          H  -2.03867273   0.75372294   0.00000000
...          H   1.57598558  -0.38252146  -0.75856129
...          H   1.57598558  -0.38252146   0.75856129
...          '''
... )
>>> grad = disp.d3_energy(scf.RHF(mol)).run().nuc_grad_method()
converged SCF energy = -149.947191000075
>>> g = grad.kernel()
--------------- DFTD3 gradients ---------------
         x                y                z
0 O     0.0171886976     0.0506606246     0.0000000000
1 O     0.0383596853    -0.0459057549     0.0000000000
2 H    -0.0313133974    -0.0125865676    -0.0000000000
3 H     0.0066705789    -0.0380501872     0.0000000000
4 H    -0.0154527822     0.0229409425     0.0215141991
5 H    -0.0154527822     0.0229409425    -0.0215141991
----------------------------------------------

dftd3.pyscf.energy(mf: pyscf.scf.hf.SCF, **kwargs) → pyscf.scf.hf.SCF#

Apply DFT-D3 corrections to SCF or MCSCF methods by returning an instance of a new class built from the original instances class. The dispersion correction is stored in the with_dftd3 attribute of the class.

Parameters:

mf (scf.hf.SCF) – The method to which DFT-D3 corrections will be applied.
**kwargs – Keyword arguments passed to the DFTD3Dispersion class.

Return type:

The method with DFT-D3 corrections applied.

Examples

>>> from pyscf import gto, scf
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          N  -1.57871857  -0.04661102   0.00000000
...          N   1.57871857   0.04661102   0.00000000
...          H  -2.15862174   0.13639605   0.80956529
...          H  -0.84947130   0.65819321   0.00000000
...          H  -2.15862174   0.13639605  -0.80956529
...          H   2.15862174  -0.13639605  -0.80956529
...          H   0.84947130  -0.65819321   0.00000000
...          H   2.15862174  -0.13639605   0.80956529
...          '''
... )
>>> mf = disp.d3_energy(scf.RHF(mol)).run()
converged SCF energy = -110.932603617026
>>> mf.kernel()
-110.93260361702605

dftd3.pyscf.grad(scf_grad: pyscf.grad.rhf.GradientsBase, **kwargs)#

Apply DFT-D3 corrections to SCF or MCSCF nuclear gradients methods by returning an instance of a new class built from the original class. The dispersion correction is stored in the with_dftd3 attribute of the class.

Parameters:

scf_grad (rhf_grad.Gradients) – The method to which DFT-D3 corrections will be applied.
**kwargs – Keyword arguments passed to the DFTD3Dispersion class.

Return type:

The method with DFT-D3 corrections applied.

Examples

>>> from pyscf import gto, scf
>>> import dftd3.pyscf as disp
>>> mol = gto.M(
...     atom='''
...          O  -1.65542061  -0.12330038   0.00000000
...          O   1.24621244   0.10268870   0.00000000
...          H  -0.70409026   0.03193167   0.00000000
...          H  -2.03867273   0.75372294   0.00000000
...          H   1.57598558  -0.38252146  -0.75856129
...          H   1.57598558  -0.38252146   0.75856129
...          '''
... )
>>> grad = disp.d3_energy(scf.RHF(mol)).run().nuc_grad_method()
converged SCF energy = -149.947191000075
>>> g = grad.kernel()
--------------- DFTD3 gradients ---------------
         x                y                z
0 O     0.0171886976     0.0506606246     0.0000000000
1 O     0.0383596853    -0.0459057549     0.0000000000
2 H    -0.0313133974    -0.0125865676    -0.0000000000
3 H     0.0066705789    -0.0380501872     0.0000000000
4 H    -0.0154527822     0.0229409425     0.0215141991
5 H    -0.0154527822     0.0229409425    -0.0215141991
----------------------------------------------

PySCF Support

Contents

PySCF Support#