def handle_mole_cell(self, entry_name, ctx):
config = self.config[entry_name]
klass = entry_name.split('-')[0]
if self.dry_run:
args = ',\n '.join(_assignment_statements(config))
if ctx is None:
if klass == 'Mole':
ctx = 'mol'
else:
ctx = 'cell'
print('%s = pyscf.M(%s)' % (ctx, args))
else:
print('%s.build(%s)' % (ctx, args))
self._ctx.append(ctx)
else:
if ctx is None:
if 'verbose' in config:
ctx = pyscf.M(**config)
else:
ctx = pyscf.M(**config, verbose=2)
else:
ctx.build(**config)
self._ctx.append(ctx)
self.extract_results(entry_name, ctx)
return ctx
def test_rmindo(self):
mol = pyscf.M(atom=[(8, (0, 0, 0)), (1, (1., 0, 0)), (1, (0, 1., 0))])
mf = semiempirical.RMINDO3(mol).run(conv_tol=1e-6)
self.assertAlmostEqual(mf.e_heat_formation, -48.82621264564841, 6)
mol = pyscf.M(atom=[(6, (0, 0,
0)), (1, (1., 0,
0)), (1,
(0, 1.,
0)), (1,
(0, 0,
1.)), (1, (0, 0,
-1.))])
mf = semiempirical.RMINDO3(mol).run(conv_tol=1e-6)
self.assertAlmostEqual(mf.e_heat_formation, 75.76019731515225, 6)
def tddft(geom, confId=None, method='B3LYP', basis='def2-svp', nstates=5):
"""
Run TD-DFT with pyscf
:param geom: an RDKit.Mol or a string
:param method: method to use
:param basis: basis to use
:param nstates: number of excited states to compute
:param confId: the ID of the conformer to optimize, if needed
"""
if not isinstance(geom, str):
assert confId
geom = make_geometry(geom, confId)
mol = pyscf.M(
atom=geom,
basis=basis,
symmetry=True,
)
mf = mol.RKS()
mf.xc = method
mf.run()
mytd = mf.TDDFT()
mytd.nstates = nstates
mytd.run()
mytd.analyze()
def get_homolumo(molstr, charge=0, verbose=0, tol=1e-6):
"""Get the H**O-1, H**O, LUMO, LUMO+1 energies in eV using MINDO3.
See https://pyscf.org/quickstart.html for more information.
Parameters
----------
molstr : str
Input string for pySCF containing elements and positions in Angstroms
(e.g., "C 0.0 0.0 0.0; H 1.54 0.0 0.0")
charge : int, default 0
If the molecule which we are calculating the energies of has a charge,
it can be specified.
verbose : int, default 0
Verbosity level of the MINDO calculation output. 0 will silence output,
4 will show convergence.
tol : float, default 1e-6
Tolerance of the MINDO convergence.
Returns
-------
numpy.ndarray
Array containing H**O-1, H**O, LUMO, LUMO+1 energies in eV
"""
mol = pyscf.M(atom=molstr, charge=charge)
mf = MINDO3(mol).run(verbose=verbose, conv_tol=tol)
occ = mf.get_occ()
i_lumo = np.argmax(occ < 1)
energies = mf.mo_energy[i_lumo - 2:i_lumo + 2]
energies *= 27.2114 # convert Eh to eV
return energies
def v2rdm_pyscf():
# pyscf reference
np.set_printoptions(linewidth=500)
basis = 'STO-3G'
mol = pyscf.M(
atom='B 0 0 0; H 0 0 {}'.format(1.1),
basis=basis)
mf = mol.RHF()
mf.verbose = 3
mf.run()
# make h1 and spatial integrals in MO basis
eri = ao2mo.kernel(mol, mf.mo_coeff)
eri = ao2mo.restore(1, eri, mf.mo_coeff.shape[1])
# this produces spatial MO h1 integrals
h1 = reduce(np.dot, (mf.mo_coeff.T, mf.get_hcore(), mf.mo_coeff))
# these parameters are hard-coded for now
nalpha = 3
nbeta = 3
nmo = 6
# hilbert options
psi4.set_module_options('hilbert', {
#'sdp_solver': 'rrsdp',
'positivity': 'dqg',
'r_convergence': 1e-5,
'e_convergence': 1e-4,
'maxiter': 20000,
})
# grab options object
options = psi4.core.get_options()
options.set_current_module('HILBERT')
v2rdm_pyscf = hilbert.v2RDMHelper(nalpha,nbeta,nmo,h1.flatten(),eri.flatten(),options)
current_energy = v2rdm_pyscf.compute_energy()
return current_energy
def CCSDExample():
print("\nSimple CCSD example with H2.");
# set up geometry of the H2 molecule
mol = ps.M(
unit = 'Bohr',
atom = 'H 0 0 0; H 0 0 1.4', # near gd state
basis = 'ccpvdz',
verbose = 3 ); #supress output for this example
print("\nH2 geometry =\n",mol.atom_coords() );
# use restricted HF to approx energy of HF gd state
EHF = mol.RHF().run();
print("\nEHF = mol.RHF().run() computes the HF gd state energy.",
"\nAccess it using EHF.kernel(): E_HF = %.5g" %EHF.kernel() );
# use coupled cluster singles doubles to approx correlation energy
ECC = EHF.CCSD().run();
print("\nECC = mf.CCSD().run() computes the correlation energy.",
"\nAccess it using ECC.e_corr: ECC = %.5g" %ECC.e_corr );
#!/usr/bin/env python
'''
Compute electron density in real space.
See also the example dft/31-xc_value_on_grid.py
'''
import numpy as np
import pyscf
mol = pyscf.M(atom='''
O 0 0. 0.
H 0 -0.757 0.587
H 0 0.757 0.587''',
basis='ccpvdz')
cc = mol.CCSD().run()
# Put 100x100x100 uniform grids in a box. The unit for coordinates is Bohr
xs = np.arange(-5, 5, .1)
ys = np.arange(-5, 5, .1)
zs = np.arange(-5, 5, .1)
grids = pyscf.lib.cartesian_prod([xs, ys, zs])
# Compute density matrix and evaluate the electron density with it.
# Note the density matrix has to be in AO basis
dm = cc.make_rdm1(ao_repr=True)
ao_value = pyscf.dft.numint.eval_ao(mol, grids, deriv=0)
rho = pyscf.dft.numint.eval_rho(mol, ao_value, dm)
print('The shape of the electron density', rho.shape)
#!/usr/bin/env python
'''
A simple example to run an NVE BOMD simulation.
'''
import pyscf
import pyscf.md
mol = pyscf.M(atom='O 0 0 0; O 0 0 1.2', verbose=3, basis='ccpvdz', spin=2)
myhf = mol.RHF().run()
# 6 orbitals, 8 electrons
mycas = myhf.CASSCF(6, 8)
myscanner = mycas.nuc_grad_method().as_scanner()
# Generate the integrator
# sets the time step to 5 a.u. and will run for 10 steps
# or for 50 a.u.
myintegrator = pyscf.md.NVE(myscanner, dt=5, steps=10)
def my_callback(local_dict):
local_dict['scanner'].base.verbose = 4
local_dict['scanner'].base.analyze()
local_dict['scanner'].base.verbose = 3
myintegrator.callback = my_callback
myintegrator.run()
#!/usr/bin/env python
'''
PySCF doesn't have its own input parser. The input file is a Python program.
Before going throught the rest part, be sure the PySCF path is added in PYTHONPATH.
'''
import pyscf
# mol is an object to hold molecule information.
mol = pyscf.M(
verbose=4,
output='out_h2o',
atom='''
o 0 0 0
h 0 -.757 .587
h 0 .757 .587''',
basis='6-31g',
)
# For more details, see pyscf/gto/mole.py and pyscf/examples/gto
#
# The package follow the convention that each method has its class to hold
# control parameters. The calculation can be executed by the kernel function.
# Eg, to do Hartree-Fock, (1) create HF object, (2) call kernel function
#
mf = mol.RHF()
print('E=%.15g' % mf.kernel())
#
# The above code can be simplified using stream operations.
#!/usr/bin/env python
import pyscf
from pyscf.tools.mo_mapping import mo_comps
from benchmarking_utils import setup_logger, get_cpu_timings
log = setup_logger()
for bas in ('3-21g', '6-31g**', 'cc-pVTZ', 'ANO-Roos-TZ'):
mol = pyscf.M(atom='''
c 1.217739890298750 -0.703062453466927 0.000000000000000
h 2.172991468538160 -1.254577209307266 0.000000000000000
c 1.217739890298750 0.703062453466927 0.000000000000000
h 2.172991468538160 1.254577209307266 0.000000000000000
c 0.000000000000000 1.406124906933854 0.000000000000000
h 0.000000000000000 2.509154418614532 0.000000000000000
c -1.217739890298750 0.703062453466927 0.000000000000000
h -2.172991468538160 1.254577209307266 0.000000000000000
c -1.217739890298750 -0.703062453466927 0.000000000000000
h -2.172991468538160 -1.254577209307266 0.000000000000000
c 0.000000000000000 -1.406124906933854 0.000000000000000
h 0.000000000000000 -2.509154418614532 0.000000000000000
''',
basis=bas)
cpu0 = get_cpu_timings()
mf = mol.RHF().run()
cpu0 = log.timer('C6H6 %s RHF' % bas, *cpu0)
mymp2 = mf.MP2().run()
cpu0 = log.timer('C6H6 %s MP2' % bas, *cpu0)
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
Active space can be adjusted by specifing the number of orbitals for each irrep.
'''
import pyscf
mol = pyscf.M(
atom='N 0 0 0; N 0 0 2',
basis='ccpvtz',
symmetry=True,
)
myhf = mol.RHF()
myhf.kernel()
mymc = myhf.CASSCF(8, 4)
# Select active orbitals which have the specified symmetry
# 2 E1gx orbitals, 2 E1gy orbitals, 2 E1ux orbitals, 2 E1uy orbitals
cas_space_symmetry = {'E1gx': 2, 'E1gy': 2, 'E1ux': 2, 'E1uy': 2}
mo = pyscf.mcscf.sort_mo_by_irrep(mymc, myhf.mo_coeff, cas_space_symmetry)
mymc.kernel(mo)
mol = pyscf.M(
atom='Ti',
basis='ccpvdz',
symmetry=True,
spin=2,
)
from pyscf import lib, scf
scf_kernel = scf.hf.SCF.kernel
def kernel(self, dm0=None, **kwargs):
scf_kernel(self, dm0, **kwargs)
if not self.converged and not self.level_shift:
with lib.temporary_env(self, level_shift=.2):
lib.logger.note(self, 'DIIS does not converge. Try level shift')
scf_kernel(self, self.make_rdm1(), **kwargs)
if not self.converged:
lib.logger.note(self, 'DIIS does not converge. Try SOSCF')
mf1 = self.newton().run()
# Note: delete mf1._scf because it leads to circular reference to self.
del mf1._scf
self.__dict__.update(mf1.__dict__)
return self.e_tot
scf.hf.SCF.kernel = kernel
# Using the patched SCF kernel
import pyscf
pyscf.M(atom='Ne', basis='ccpvdz',
verbose=3).RKS().density_fit().run(max_cycle=3)
def main():
from itertools import product
import pyscf
import openfermion as of
from openfermion.chem.molecular_data import spinorb_from_spatial
from openfermionpyscf import run_pyscf
from pyscf.cc.addons import spatial2spin
import numpy as np
basis = 'cc-pvdz'
mol = pyscf.M(atom='H 0 0 0; B 0 0 {}'.format(1.6), basis=basis)
mf = mol.RHF().run()
mycc = mf.CCSD().run()
print('CCSD correlation energy', mycc.e_corr)
molecule = of.MolecularData(geometry=[['H', (0, 0, 0)], ['B',
(0, 0, 1.6)]],
basis=basis,
charge=0,
multiplicity=1)
molecule = run_pyscf(molecule, run_ccsd=True)
oei, tei = molecule.get_integrals()
norbs = int(mf.mo_coeff.shape[1])
occ = mf.mo_occ
nele = int(sum(occ))
nocc = nele // 2
assert np.allclose(np.transpose(mycc.t2, [1, 0, 3, 2]), mycc.t2)
soei, stei = spinorb_from_spatial(oei, tei)
astei = np.einsum('ijkl', stei) - np.einsum('ijlk', stei)
# put in physics notation. OpenFermion stores <12|2'1'>
gtei = astei.transpose(0, 1, 3, 2)
eps = np.kron(molecule.orbital_energies, np.ones(2))
n = np.newaxis
o = slice(None, 2 * nocc)
v = slice(2 * nocc, None)
e_abij = 1 / (-eps[v, n, n, n] - eps[n, v, n, n] + eps[n, n, o, n] +
eps[n, n, n, o])
e_ai = 1 / (-eps[v, n] + eps[n, o])
fock = soei + np.einsum('piiq->pq', astei[:, o, o, :])
hf_energy = 0.5 * np.einsum('ii', (fock + soei)[o, o])
hf_energy_test = 1.0 * einsum('ii', fock[o, o]) - 0.5 * einsum(
'ijij', gtei[o, o, o, o])
print(hf_energy_test, hf_energy)
assert np.isclose(hf_energy + molecule.nuclear_repulsion,
molecule.hf_energy)
g = gtei
nsvirt = 2 * (norbs - nocc)
nsocc = 2 * nocc
t1f, t2f = kernel(np.zeros((nsvirt, nsocc)),
np.zeros((nsvirt, nsvirt, nsocc, nsocc)), fock, g, o, v,
e_ai, e_abij)
print(ccsd_energy(t1f, t2f, fock, g, o, v) - hf_energy)
t1f, t2f = kernel(np.zeros((nsvirt, nsocc)),
np.zeros((nsvirt, nsvirt, nsocc, nsocc)),
fock,
g,
o,
v,
e_ai,
e_abij,
diis_size=8,
diis_start_cycle=4)
print(ccsd_energy(t1f, t2f, fock, g, o, v) - hf_energy)
'''
A simple example of using polarizable embedding model in the mean-field
calculations. This example requires the cppe library
GitHub: https://github.com/maxscheurer/cppe
Code: 10.5281/zenodo.3345696
Publication: https://doi.org/10.1021/acs.jctc.9b00758
The CPPE library can be installed via:
pip install git+https://github.com/maxscheurer/cppe.git
The potfile required by this example can be generated with the script
04-pe_potfile_from_pyframe.py
'''
import pyscf
from pyscf import solvent
mol = pyscf.M(atom='''
C 0.000000 0.000000 -0.542500
O 0.000000 0.000000 0.677500
H 0.000000 0.9353074360871938 -1.082500
H 0.000000 -0.9353074360871938 -1.082500
''',
verbose = 4)
mf = mol.UKS()
mf.xc = 'b3lyp'
mf = solvent.PE(mf, '4NP_in_water/4NP_in_water.pot')
mf.run()
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
Symmetry is not immutable
CASSCF solver can have different symmetry to reference Hartree-Fock calculation.
'''
import pyscf
mol = pyscf.M(
atom='Cr 0 0 0',
basis='cc-pvtz',
spin=6,
symmetry=True,
)
myhf = mol.RHF()
myhf.irrep_nelec = {
's+0': (4, 3),
'd-2': (1, 0),
'd-1': (1, 0),
'd+0': (1, 0),
'd+1': (1, 0),
'd+2': (1, 0)
}
myhf.kernel()
myhf.mol.build(0, 0, symmetry='D2h')
mymc = myhf.CASSCF(9, 6)
#!/usr/bin/env python
'''
State average over custom FCI solvers
This example shows how to put custom FCI solvers into one state-average solver
using the method state_average_mix_.
'''
import numpy as np
import pyscf
mol = pyscf.M(atom=[
['C', (0., 0., -.485)],
['C', (0., 0., .485)],
],
basis='cc-pvdz',
symmetry=True)
mf = mol.RHF()
mf.irrep_nelec = {
'A1g': 4,
'E1gx': 0,
'E1gy': 0,
'A1u': 4,
'E1uy': 2,
'E1ux': 2,
'E2gx': 0,
'E2gy': 0,
'E2uy': 0,
'E2ux': 0
}
ehf = mf.kernel()
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
Example for the value of FCI wave function on specific electrons coordinates
'''
import numpy as np
import pyscf
from pyscf import fci
mol = pyscf.M(atom='H 0 0 0; H 0 0 1.1', basis='6-31g', verbose=0)
mf = mol.RHF().run()
e1, ci1 = fci.FCI(mol, mf.mo_coeff).kernel()
print('FCI energy', e1)
# coordinates for all electrons in the molecule
e_coords = np.random.rand(mol.nelectron, 3)
ao_on_grid = mol.eval_gto('GTOval', e_coords)
mo_on_grid = ao_on_grid.dot(mf.mo_coeff)
# View mo_on_grid as a transformation from MO to grid basis
mo_grid_ovlp = mo_on_grid.T
# ci_on_grid gives the value on e_grids for each CI determinant
ci_on_grid = fci.addons.transform_ci(ci1, mol.nelec, mo_grid_ovlp)
print(f'For electrons on grids\n{e_coords}\nCI value = {ci_on_grid.sum()}')
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
Overlap of two FCI wave functions
'''
from functools import reduce
import numpy
import pyscf
myhf1 = pyscf.M(atom='H 0 0 0; F 0 0 1.1', basis='6-31g',
verbose=0).RHF().run()
e1, ci1 = pyscf.fci.FCI(myhf1).kernel()
print('FCI energy of mol1', e1)
myhf2 = pyscf.M(atom='H 0 0 0; F 0 0 1.2', basis='6-31g',
verbose=0).RHF().run()
e2, ci2 = pyscf.fci.FCI(myhf2).kernel()
print('FCI energy of mol2', e2)
myhf3 = pyscf.M(atom='H 0 0 0; F 0 0 1.3', basis='6-31g',
verbose=0).UHF().run()
e3, ci3 = pyscf.fci.FCI(myhf3).kernel()
print('FCI energy of mol3', e3)
#
# Overlap between FCI wfn of different geometries
#
s12 = pyscf.gto.intor_cross('int1e_ovlp', myhf1.mol, myhf2.mol)
import time
import pyscf
from pyscf.tools.mo_mapping import mo_comps
log = pyscf.lib.logger.Logger(verbose=5)
with open('/proc/cpuinfo') as f:
for line in f:
if 'model name' in line:
log.note(line[:-1])
break
with open('/proc/meminfo') as f:
log.note(f.readline()[:-1])
log.note('OMP_NUM_THREADS=%s\n', os.environ.get('OMP_NUM_THREADS', None))
for bas in ('3-21g', '6-31g*', 'cc-pVTZ', 'ANO-Roos-TZ'):
mol = pyscf.M(atom='N 0 0 0; N 0 0 1.1', basis=bas)
cpu0 = time.clock(), time.time()
mf = mol.RHF().run()
cpu0 = log.timer('N2 %s RHF' % bas, *cpu0)
mymp2 = mf.MP2().run()
cpu0 = log.timer('N2 %s MP2' % bas, *cpu0)
mymc = mf.CASSCF(4, 4)
idx_2pz = mo_comps('2p[xy]', mol, mf.mo_coeff).argsort()[-4:]
mo = mymc.sort_mo(idx_2pz, base=0)
mymc.kernel(mo)
cpu0 = log.timer('N2 %s CASSCF' % bas, *cpu0)
mycc = mf.CCSD().run()
cell0 = pyscf.M(a=np.eye(3) * boxlen,
atom="""
O 12.235322 1.376642 10.869880
O 6.445390 3.706940 8.650794
O 0.085977 2.181322 8.276663
O 12.052554 2.671366 2.147199
O 12.250036 4.190930 12.092014
O 7.187422 0.959062 4.733469
O 8.346457 7.210040 4.667644
O 12.361546 11.527875 8.106887
O 3.299984 4.440816 9.193275
O 2.855829 3.759909 6.552815
O 1.392494 6.362753 0.586172
O 1.858645 8.694013 2.068738
O 3.770231 12.094519 8.652183
O 6.432508 3.669828 2.772418
O 1.998724 1.820217 4.876440
O 8.248581 2.404730 6.931303
O 5.753814 3.360029 12.461534
O 11.322212 5.649239 2.236798
O 4.277318 2.113956 10.590808
O 5.405015 3.349247 5.484702
O 6.493278 11.869958 0.684912
O 3.275250 2.346576 2.425241
O 7.981003 6.352512 7.507970
O 5.985990 6.512854 12.194648
O 10.636714 11.856872 12.209540
O 9.312283 3.670384 3.508594
O 1.106885 5.830301 6.638695
O 8.008007 3.326363 10.869818
O 12.403000 9.687405 11.761901
O 4.219782 7.085315 8.153470
O 3.781557 8.203821 11.563272
O 11.088898 4.532081 7.809475
O 10.387548 8.408890 1.017882
O 1.979016 6.418091 10.374159
O 4.660547 0.549666 5.617403
O 8.745880 12.256257 8.089383
O 2.662041 10.489890 0.092980
O 7.241661 10.471815 4.226946
O 2.276827 0.276647 10.810417
O 8.887733 0.946877 1.333885
O 1.943554 8.088552 7.567650
O 9.667942 8.056759 9.868847
O 10.905491 8.339638 6.484782
O 3.507733 4.862402 1.557439
O 8.010457 8.642846 12.055969
O 8.374446 10.035932 6.690309
O 5.635247 6.076875 5.563993
O 11.728434 1.601906 5.079475
O 9.771134 9.814114 3.548703
O 3.944355 10.563450 4.687536
O 0.890357 6.382287 4.065806
O 6.862447 6.425182 2.488202
O 3.813963 6.595122 3.762649
O 6.562448 8.295463 8.807182
O 9.809455 0.143325 3.886553
O 4.117074 11.661225 2.221679
O 5.295317 8.735561 2.763183
O 9.971999 5.379339 5.340378
O 12.254708 8.643874 3.957116
O 2.344274 10.761274 6.829162
O 7.013416 0.643488 10.518797
O 5.152349 10.233624 10.359388
O 11.184278 5.884064 10.298279
O 12.252335 8.974142 9.070831
H 12.415139 2.233125 11.257611
H 11.922476 1.573799 9.986994
H 5.608192 3.371543 8.971482
H 6.731226 3.060851 8.004962
H -0.169205 1.565594 7.589645
H -0.455440 2.954771 8.118939
H 12.125168 2.826463 1.205443
H 12.888828 2.969761 2.504745
H 11.553255 4.386613 11.465566
H 12.818281 4.960808 12.067151
H 7.049495 1.772344 4.247898
H 6.353019 0.798145 5.174047
H 7.781850 7.384852 5.420566
H 9.103203 6.754017 5.035898
H 12.771232 11.788645 8.931744
H 12.018035 10.650652 8.276334
H 3.557245 3.792529 9.848846
H 2.543844 4.884102 9.577958
H 2.320235 4.521250 6.329813
H 2.872128 3.749963 7.509824
H 1.209685 7.121391 1.140501
H 2.238885 6.038801 0.894245
H 2.763109 8.856353 2.336735
H 1.329379 9.047369 2.783755
H 4.315639 11.533388 9.203449
H 3.098742 12.433043 9.244412
H 5.987369 3.448974 3.590530
H 5.813096 3.419344 2.086985
H 1.057126 1.675344 4.969379
H 2.248496 2.292119 5.670892
H 8.508264 1.653337 7.464411
H 8.066015 2.034597 6.067646
H 5.197835 2.915542 11.821572
H 6.630900 3.329981 12.079371
H 10.788986 6.436672 2.127933
H 11.657923 5.463602 1.359832
H 3.544476 1.634958 10.977765
H 4.755770 1.455054 10.087655
H 4.465371 3.375459 5.665294
H 5.682663 4.264430 5.524498
H 6.174815 11.778676 1.582954
H 5.713640 12.089924 0.174999
H 3.476076 1.498708 2.028983
H 2.730229 2.134295 3.182949
H 7.119624 5.936450 7.474030
H 8.536492 5.799405 6.958665
H 5.909499 5.717477 11.667621
H 6.125402 6.196758 13.087330
H 11.203499 12.513536 11.804844
H 10.260930 12.300153 12.970145
H 9.985036 3.927685 2.878172
H 8.545584 3.468329 2.972331
H 1.399882 6.620092 7.093246
H 0.963561 6.112523 5.735345
H 8.067363 3.674002 9.979955
H 8.000737 2.375959 10.756190
H 11.821629 10.402510 12.020482
H 12.206854 8.983242 12.379892
H 3.461473 7.606485 7.889688
H 3.844478 6.304711 8.560946
H 3.179884 7.585614 11.148494
H 4.401957 7.652030 12.039573
H 11.573777 5.053211 7.169515
H 10.342076 4.186083 7.320831
H 10.065640 8.919194 1.760981
H 9.629585 8.322499 0.439729
H 1.396302 6.546079 9.625630
H 1.405516 6.479759 11.138049
H 4.024008 1.232518 5.405828
H 4.736858 0.579881 6.571077
H 9.452293 12.313381 8.732772
H 8.976559 11.502788 7.545965
H 1.834701 10.012311 0.153462
H 3.295197 9.836403 -0.204175
H 7.056724 11.401702 4.095264
H 6.499038 10.020287 3.825865
H 1.365541 0.487338 11.013887
H 2.501591 -0.428131 11.417871
H 8.644279 1.812362 1.005409
H 8.142674 0.388030 1.112955
H 1.272659 8.365063 8.191888
H 2.142485 8.877768 7.063867
H 8.961493 7.826192 9.265523
H 9.227102 8.487654 10.601118
H 10.150144 7.758934 6.392768
H 10.596082 9.187988 6.167290
H 3.463106 4.096188 2.129414
H 3.919461 4.539801 0.755791
H 7.418998 9.394959 12.028876
H 7.430413 7.883095 12.106546
H 7.972905 10.220334 5.841196
H 7.675111 9.631498 7.203725
H 5.332446 6.381336 6.419473
H 5.000025 6.434186 4.943466
H 11.575078 2.271167 4.412540
H 11.219802 0.847030 4.783357
H 8.865342 9.721516 3.843998
H 10.000732 10.719285 3.758898
H 3.186196 10.476397 5.265333
H 4.407331 11.335128 5.013723
H 0.558187 7.255936 3.859331
H 0.341672 5.789383 3.552346
H 7.459933 6.526049 3.229193
H 6.696228 5.483739 2.440372
H 3.864872 6.313007 2.849385
H 2.876419 6.621201 3.953862
H 5.631529 8.079145 8.753997
H 7.003296 7.568245 8.367822
H 9.615413 0.527902 3.031755
H 8.962985 0.109366 4.332162
H 3.825854 11.139182 1.474087
H 4.063988 11.063232 2.967211
H 5.784391 7.914558 2.708486
H 4.780461 8.655167 3.566110
H 10.880659 5.444664 5.046607
H 9.593331 4.687991 4.797350
H 11.562317 8.960134 3.376765
H 11.926084 8.816948 4.839320
H 2.856874 11.297981 7.433660
H 1.492332 11.195517 6.786033
H 7.145820 0.090200 9.749009
H 7.227275 0.077690 11.260665
H 4.662021 9.538430 10.798155
H 5.994537 9.833472 10.142985
H 10.544299 6.595857 10.301445
H 11.281750 5.653082 9.374494
H 12.103020 8.841164 10.006916
H 11.491592 8.576221 8.647557
""",
basis='gth-tzv2p',
pseudo='gth-pade',
max_memory=50000,
precision=1e-6)
#!/usr/bin/env python
'''
Analytical nuclear gradients of state-average CASSCF
More examples can be found in 12-excited_state_casscf_grad.py
'''
import pyscf
mol = pyscf.M(atom='N 0 0 0; N 0 0 1.2', basis='ccpvdz', verbose=5)
mf = mol.RHF().run()
sa_mc = mf.CASSCF(4, 4).state_average_([0.5, 0.5]).run()
print('State-average CASSCF total energy', sa_mc.e_tot)
sa_mc_grad = sa_mc.Gradients()
# The state-averaged nuclear gradients
de_avg = sa_mc_grad.kernel()
# Nuclear gradients for state 1
de_0 = sa_mc_grad.kernel(state=0)
# Nuclear gradients for state 2
de_1 = sa_mc_grad.kernel(state=1)
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
CCSD and CCSD(T) lambda equation
'''
import pyscf
mol = pyscf.M(atom='''
O 0. 0. 0.
H 0. -0.757 0.587
H 0. 0.757 0.587''',
basis='cc-pvdz')
mf = mol.RHF().run()
cc = mf.CCSD().run()
#
# Solutions for CCSD Lambda equations are saved in cc.l1 and cc.l2
#
cc.solve_lambda()
print(cc.l1.shape)
print(cc.l2.shape)
###
#
# Compute CCSD(T) lambda with ccsd_t-slow implementation
# (as of pyscf v1.7)
#
from pyscf.cc import ccsd_t_lambda_slow as ccsd_t_lambda
conv, l1, l2 = ccsd_t_lambda.kernel(cc, cc.ao2mo(), cc.t1, cc.t2, tol=1e-8)
#!/usr/bin/env python
#
# Author: Qiming Sun <*****@*****.**>
#
'''
A simple example to run CCSD(T) and UCCSD(T) calculation.
'''
import pyscf
mol = pyscf.M(atom='H 0 0 0; F 0 0 1.1', basis='ccpvdz')
mf = mol.RHF().run()
mycc = mf.CCSD().run()
et = mycc.ccsd_t()
print('CCSD(T) correlation energy', mycc.e_corr + et)
mf = mol.UHF().run()
mycc = mf.CCSD().run()
et = mycc.ccsd_t()
print('UCCSD(T) correlation energy', mycc.e_corr + et)
def test_umindo(self):
mol = pyscf.M(atom=[(8, (0, 0, 0)), (1, (1., 0, 0))], spin=1)
mf = semiempirical.UMINDO3(mol).run(conv_tol=1e-6)
self.assertAlmostEqual(mf.e_heat_formation, 18.08247965492137)
#!/usr/bin/env python
import os
import time
import pyscf
from pyscf.tools import c60struct
log = pyscf.lib.logger.Logger(verbose=5)
with open('/proc/cpuinfo') as f:
for line in f:
if 'model name' in line:
log.note(line[:-1])
break
with open('/proc/meminfo') as f:
log.note(f.readline()[:-1])
log.note('OMP_NUM_THREADS=%s\n', os.environ.get('OMP_NUM_THREADS', None))
for bas in ('6-31g**', 'cc-pVTZ'):
mol = pyscf.M(atom=[('C', r) for r in c60struct.make60(1.46, 1.38)],
basis=bas,
max_memory=40000)
cpu0 = time.clock(), time.time()
mf = pyscf.scf.fast_newton(mol.RHF())
cpu0 = logger.timer('SOSCF/%s' % bas, *cpu0)
mf = mol.RHF().density_fit().run()
cpu0 = logger.timer('density-fitting-HF/%s' % bas, *cpu0)
#!/usr/bin/env python
'''
A simple example to run CAM-B3LYP TDDFT calculation.
You need to switch to xcfun library if you found an error like
NotImplementedError: libxc library does not support derivative order 2 for camb3lyp
This functional derivative is supported in the xcfun library.
The following code can be used to change the libxc library to xcfun library:
from pyscf.dft import xcfun
mf._numint.libxc = xcfun
'''
import pyscf
mol = pyscf.M(
atom='H 0 0 0; F 0 0 1.1',
basis='6-31g(d,p)',
symmetry=True,
)
mf = mol.RKS()
mf.xc = 'camb3lyp'
mf.run()
# Note you need to switch to xcfun library for cam-b3lyp tddft
mf._numint.libxc = pyscf.dft.xcfun
mytd = mf.TDDFT()
mytd.kernel()
#!/usr/bin/env python
'''
PySCF doesn't have its own input parser. The input file is a Python program.
Before going throught the rest part, be sure the PySCF path is added in PYTHONPATH.
'''
import pyscf
# mol is an object to hold molecule information.
mol = pyscf.M(
verbose=4,
output='out_h2o',
atom='''
o 0 0 0
h 0 -.757 .587
h 0 .757 .587''',
basis='6-31g',
)
# For more details, see pyscf/gto/mole.py and pyscf/examples/gto
#
# The package follow the convention that each method has its class to hold
# control parameters. The calculation can be executed by the kernel function.
# Eg, to do Hartree-Fock, (1) create HF object, (2) call kernel function
#
mf = mol.RHF()
print('E(HF)=%.15g' % mf.kernel())
#
# A post-HF method can be applied.
# Cmake flag -DWITH_RANGE_COULOMB=1 should be set
import time
import numpy
import pyscf
mol = pyscf.M(
atom='''H 0 0 0;
H 0 -1 1
H 0 1 1
H 1 0 -1''',
basis={
'H': [[0, (2., .8), (1.5, .3)], [0, (.625, 1)], [1, (.8, 1)],
[2, (1.2, 1)]]
},
)
eri = mol.intor('int2e')
with mol.with_range_coulomb(.3):
eri_lr = mol.intor('int2e')
with mol.with_range_coulomb(-.3):
eri_sr = mol.intor('int2e')
print(abs(eri_lr + eri_sr - eri).max())
A simple example to call integral transformation for given orbitals
'''
mol = gto.Mole()
mol.build(
atom='H 0 0 0; F 0 0 1.1', # in Angstrom
basis='ccpvdz',
symmetry=True,
)
myhf = scf.RHF(mol)
myhf.kernel()
orb = myhf.mo_coeff
eri_4fold = ao2mo.kernel(mol, orb)
print('MO integrals (ij|kl) with 4-fold symmetry i>=j, k>=l have shape %s' %
str(eri_4fold.shape))
#
# Starting from PySCF-1.7, the MO integrals can be computed with the code
# below.
#
import pyscf
mol = pyscf.M(
atom='H 0 0 0; F 0 0 1.1', # in Angstrom
basis='ccpvdz',
symmetry=True,
)
orb = mol.RHF().run().mo_coeff
eri_4fold = mol.ao2mo(orb)
#!/usr/bin/env python
'''
Various Pipek-Mezey localization schemes
See more discussions in paper JCTC, 10, 642 (2014); DOI:10.1021/ct401016x
'''
import pyscf
x = .63
mol = pyscf.M(atom=[['C', (0, 0, 0)], ['H', (x, x, x)], ['H', (-x, -x, x)],
['H', (-x, x, -x)], ['H', (x, -x, -x)]],
basis='ccpvtz')
mf = mol.RHF().run()
orbitals = mf.mo_coeff[:, mf.mo_occ > 0]
pm = pyscf.lo.PM(mol, orbitals, mf)
def print_coeff(local_orb):
import sys
idx = mol.search_ao_label(['C 1s', 'C 2s', 'C 2p', 'H 1s'])
ao_labels = mol.ao_labels()
labels = [ao_labels[i] for i in idx]
pyscf.tools.dump_mat.dump_rec(sys.stdout, local_orb[idx], labels)
print('---')
pm.pop_method = 'mulliken'
print_coeff(pm.kernel())
