def DMC(dft_checkfile,
output,
nconfig=1000,
start_from=None,
S=None,
client=None,
npartitions=None,
jastrow_kws=None,
slater_kws=None,
dmc_kws=None,
accumulators=None):
if dmc_kws is None:
dmc_kws = {}
mol, mf = pyqmc.recover_pyscf(dft_checkfile)
if S is not None:
mol = pyqmc.get_supercell(mol, np.asarray(S))
wf, _ = pyqmc.generate_wf(mol,
mf,
jastrow_kws=jastrow_kws,
slater_kws=slater_kws)
if start_from is not None:
pyqmc.read_wf(wf, start_from)
configs = pyqmc.initial_guess(mol, nconfig)
pyqmc.rundmc(wf,
configs,
accumulators=generate_accumulators(mol, mf, **accumulators),
verbose=True,
hdf_file=output,
client=client,
npartitions=npartitions,
**dmc_kws)
def OPTIMIZE(
dft_checkfile,
output,
anchors=None,
nconfig=1000,
ci_checkfile=None,
start_from=None,
S=None,
client=None,
npartitions=None,
jastrow_kws=None,
slater_kws=None,
linemin_kws=None,
):
if linemin_kws is None:
linemin_kws = {}
target_root = 0
if ci_checkfile is None:
mol, mf = pyqmc.recover_pyscf(dft_checkfile)
mc = None
else:
mol, mf, mc = pyqmc.recover_pyscf(dft_checkfile,
ci_checkfile=ci_checkfile)
mc.ci = mc.ci[target_root]
if S is not None:
mol = pyqmc.get_supercell(mol, np.asarray(S))
wf, to_opt = pyqmc.generate_wf(mol,
mf,
mc=mc,
jastrow_kws=jastrow_kws,
slater_kws=slater_kws)
if start_from is not None:
pyqmc.read_wf(wf, start_from)
configs = pyqmc.initial_guess(mol, nconfig)
acc = pyqmc.gradient_generator(mol, wf, to_opt)
if anchors is None:
pyqmc.line_minimization(wf,
configs,
acc,
verbose=True,
hdf_file=output,
client=client,
npartitions=npartitions,
**linemin_kws)
else:
wfs = [pyqmc.read_wf(copy.deepcopy(wf), a) for a in anchors]
wfs.append(wf)
pyqmc.optimize_orthogonal(
wfs,
configs,
acc,
# verbose=True,
hdf_file=output,
client=client,
npartitions=npartitions,
**linemin_kws)
def OPTIMIZE(
dft_checkfile,
output,
nconfig=1000,
start_from=None,
S=None,
client=None,
npartitions=None,
jastrow_kws=None,
slater_kws=None,
linemin_kws=None,
):
if linemin_kws is None:
linemin_kws = {}
mol, mf = pyqmc.recover_pyscf(dft_checkfile)
if S is not None:
mol = pyqmc.get_supercell(mol, np.asarray(S))
wf, to_opt = pyqmc.generate_wf(mol,
mf,
jastrow_kws=jastrow_kws,
slater_kws=slater_kws)
if start_from is not None:
pyqmc.read_wf(wf, start_from)
configs = pyqmc.initial_guess(mol, nconfig)
acc = pyqmc.gradient_generator(mol, wf, to_opt)
pyqmc.line_minimization(wf,
configs,
acc,
verbose=True,
hdf_file=output,
client=client,
npartitions=npartitions,
**linemin_kws)
def VMC(
dft_checkfile,
output,
nconfig=1000,
ci_checkfile=None,
start_from=None,
S=None,
client=None,
npartitions=None,
jastrow_kws=None,
slater_kws=None,
vmc_kws=None,
accumulators=None,
):
if vmc_kws is None:
vmc_kws = {}
target_root = 0
if ci_checkfile is None:
mol, mf = pyqmc.recover_pyscf(dft_checkfile)
mc = None
else:
mol, mf, mc = pyqmc.recover_pyscf(dft_checkfile,
ci_checkfile=ci_checkfile)
mc.ci = mc.ci[target_root]
if S is not None:
print("S", S)
mol = pyqmc.get_supercell(mol, np.asarray(S))
if accumulators is None:
accumulators = {}
wf, _ = pyqmc.generate_wf(mol,
mf,
mc=mc,
jastrow_kws=jastrow_kws,
slater_kws=slater_kws)
if start_from is not None:
pyqmc.read_wf(wf, start_from)
configs = pyqmc.initial_guess(mol, nconfig)
pyqmc.vmc(wf,
configs,
accumulators=generate_accumulators(mol, mf, **accumulators),
verbose=True,
hdf_file=output,
client=client,
npartitions=npartitions,
**vmc_kws)
def test():
chkfile = "h2.hdf5"
optfile = "linemin.hdf5"
run_scf(chkfile)
mol, mf = pyqmc.recover_pyscf(chkfile)
noise = (np.random.random(mf.mo_coeff.shape) - 0.5) * 0.2
mf.mo_coeff = mf.mo_coeff * 1j + noise
slater_kws = {"optimize_orbitals": True}
wf, to_opt = pyqmc.generate_wf(mol, mf, slater_kws=slater_kws)
configs = pyqmc.initial_guess(mol, 100)
acc = pyqmc.gradient_generator(mol, wf, to_opt)
pyqmc.line_minimization(wf,
configs,
acc,
verbose=True,
hdf_file=optfile,
max_iterations=5)
assert os.path.isfile(optfile)
os.remove(chkfile)
os.remove(optfile)