def test_transform():
""" Just prints things out;
TODO: figure out a thing to test.
"""
from pyscf import gto, scf
import pyqmc
r = 1.54 / .529177
mol = gto.M(atom='H 0. 0. 0.; H 0. 0. %g' % r,
ecp='bfd',
basis='bfd_vtz',
unit='bohr',
verbose=1)
mf = scf.RHF(mol).run()
wf = pyqmc.slater_jastrow(mol, mf)
enacc = pyqmc.EnergyAccumulator(mol)
print(list(wf.parameters.keys()))
transform = LinearTransform(wf.parameters)
x = transform.serialize_parameters(wf.parameters)
nconfig = 10
configs = pyqmc.initial_guess(mol, nconfig)
wf.recompute(configs)
pgrad = wf.pgradient()
gradtrans = transform.serialize_gradients(pgrad)
assert gradtrans.shape[1] == len(x)
assert gradtrans.shape[0] == nconfig
def generate_wfs():
from pyscf import gto, scf
import pyqmc
mol = gto.M(
atom="O 0 0 0; H 0 -2.757 2.587; H 0 2.757 2.587", basis="bfd_vtz", ecp="bfd"
)
mf = scf.RHF(mol).run()
wf=pyqmc.slater_jastrow(mol,mf)
return mol,mf,wf
def test():
""" Optimize a Helium atom's wave function and check that it's
better than Hartree-Fock"""
mol = gto.M(atom="He 0. 0. 0.", basis="bfd_vdz", ecp="bfd", unit="bohr")
mf = scf.RHF(mol).run()
wf = slater_jastrow(mol, mf)
nconf = 500
wf, dfgrad, dfline = line_minimization(wf, initial_guess(mol, nconf),
gradient_generator(mol, wf))
dfgrad = pd.DataFrame(dfgrad)
mfen = mf.energy_tot()
enfinal = dfgrad["en"].values[-1]
enfinal_err = dfgrad["en_err"].values[-1]
assert mfen > enfinal
def setuph2(r, obdm_steps=5):
from pyscf import gto, scf, lo
from pyqmc.accumulators import LinearTransform, EnergyAccumulator
from pyqmc.obdm import OBDMAccumulator
from pyqmc.cvmc import DescriptorFromOBDM, PGradDescriptor
import itertools
# ccECP from A. Annaberdiyev et al. Journal of Chemical Physics 149, 134108 (2018)
basis = {
"H":
gto.basis.parse("""
H S
23.843185 0.00411490
10.212443 0.01046440
4.374164 0.02801110
1.873529 0.07588620
0.802465 0.18210620
0.343709 0.34852140
0.147217 0.37823130
0.063055 0.11642410
""")
}
"""
H S
0.040680 1.00000000
H S
0.139013 1.00000000
H P
0.166430 1.00000000
H P
0.740212 1.00000000
"""
ecp = {
"H":
gto.basis.parse_ecp("""
H nelec 0
H ul
1 21.24359508259891 1.00000000000000
3 21.24359508259891 21.24359508259891
2 21.77696655044365 -10.85192405303825
""")
}
mol = gto.M(atom=f"H 0. 0. 0.; H 0. 0. {r}",
unit="bohr",
basis=basis,
ecp=ecp,
verbose=5)
mf = scf.RHF(mol).run()
mo_occ = mf.mo_coeff[:, mf.mo_occ > 0]
a = lo.iao.iao(mol, mo_occ)
a = lo.vec_lowdin(a, mf.get_ovlp())
obdm_up = OBDMAccumulator(mol=mol, orb_coeff=a, nstep=obdm_steps, spin=0)
obdm_down = OBDMAccumulator(mol=mol, orb_coeff=a, nstep=obdm_steps, spin=1)
wf = pyqmc.slater_jastrow(mol, mf)
freeze = {}
for k in wf.parameters:
freeze[k] = np.zeros(wf.parameters[k].shape, dtype='bool')
print(freeze.keys())
print(wf.parameters['wf1mo_coeff_alpha'])
#this freezing allows us to easily go between bonding and
# AFM configurations.
freeze['wf1mo_coeff_alpha'][0, 0] = True
freeze['wf1mo_coeff_beta'][1, 0] = True
descriptors = {
"t": [[(1.0, (0, 1)), (1.0, (1, 0))], [(1.0, (0, 1)), (1.0, (1, 0))]],
"trace": [[(1.0, (0, 0)), (1.0, (1, 1))], [(1.0, (0, 0)),
(1.0, (1, 1))]],
}
for i in [0, 1]:
descriptors[f"nup{i}"] = [[(1.0, (i, i))], []]
descriptors[f"ndown{i}"] = [[], [(1.0, (i, i))]]
acc = PGradDescriptor(
EnergyAccumulator(mol),
LinearTransform(wf.parameters, freeze=freeze),
[obdm_up, obdm_down],
DescriptorFromOBDM(descriptors, norm=2.0),
)
return {
"wf": wf,
"acc": acc,
"mol": mol,
"mf": mf,
"descriptors": descriptors
}
mol = gto.M(atom="O 0 0 0; H 0 -2.757 2.587; H 0 2.757 2.587",
basis="bfd_vtz",
ecp="bfd")
mf = scf.RHF(mol).run()
# clean_pyscf_objects gets rid of the TextIO objects that can't
# be sent using parsl.
mol, mf = clean_pyscf_objects(mol, mf)
# It's better to load parsl after pyscf has run. Some of the
# executors have timeouts and will kill the job while pyscf is running!
parsl.load(config)
parsl.set_stream_logger(level=logging.WARNING)
# We make a Slater-Jastrow wave function and
# only optimize the Jastrow coefficients.
wf = pyqmc.slater_jastrow(mol, mf)
acc = pyqmc.gradient_generator(mol, wf, ["wf2acoeff", "wf2bcoeff"])
# Generate the initial configurations.
# Here we run VMC for a few steps with no accumulators to equilibrate the
# walkers.
configs = pyqmc.initial_guess(mol, nconf)
df, configs = distvmc(wf,
configs,
accumulators={},
nsteps=10,
npartitions=ncore)
# This uses a stochastic reconfiguration step to generate parameter changes along a line,
# then minimizes the energy along that line.
wf, dfgrad, dfline = line_minimization(