def default_multislater(mol, mf, mc):
import numpy as np
wf = MultiSlater(mol, mf, mc)
freeze = {}
freeze["det_coeff"] = np.zeros(wf.parameters["det_coeff"].shape).astype(bool)
freeze["det_coeff"][0] = True # Determinant coefficient pivot
to_opt = ["det_coeff"] # Don't have orbital coeff opt on this yet
return wf, to_opt, freeze
def default_multislater(mol, mf, mc, tol=None, optimize_orbitals=False):
import numpy as np
wf = MultiSlater(mol, mf, mc, tol)
to_opt = ["det_coeff"]
to_opt = {"det_coeff": np.ones(wf.parameters["det_coeff"].shape).astype(bool)}
to_opt["det_coeff"][0] = False # Determinant coefficient pivot
if optimize_orbitals:
for k in ["mo_coeff_alpha", "mo_coeff_beta"]:
to_opt[k] = np.ones(wf.parameters[k].shape).astype(bool)
return wf, to_opt
def test_ecp():
mol = gto.M(
atom="""C 0 0 0
C 1 0 0
""",
ecp="bfd",
basis="bfd_vtz",
)
mf = scf.RHF(mol).run()
nconf = 1000
coords = initial_guess(mol, nconf)
thresholds = [1e15, 100, 50, 20, 10, 5, 1]
label = ["S", "J", "SJ"]
ind = 0
for wf in [
PySCFSlaterUHF(mol, mf),
JastrowSpin(mol),
MultiplyWF(PySCFSlaterUHF(mol, mf), JastrowSpin(mol)),
]:
wf.recompute(coords)
print(label[ind])
ind += 1
for threshold in thresholds:
eacc = EnergyAccumulator(mol, threshold)
start = time.time()
eacc(coords, wf)
end = time.time()
print("Threshold=", threshold, np.around(end - start, 2), "s")
mc = mcscf.CASCI(mf, ncas=4, nelecas=(2, 2))
mc.kernel()
label = ["MS"]
ind = 0
for wf in [MultiSlater(mol, mf, mc)]:
wf.recompute(coords)
print(label[ind])
ind += 1
for threshold in thresholds:
eacc = EnergyAccumulator(mol, threshold)
start = time.time()
eacc(coords, wf)
end = time.time()
print("Threshold=", threshold, np.around(end - start, 2), "s")
def default_multislater(mol, mf, mc, tol=None, freeze_orb=None):
import numpy as np
# Nothing provided, nothing frozen
if freeze_orb is None:
freeze_orb = [[],[]]
wf = MultiSlater(mol, mf, mc, tol, freeze_orb)
to_opt = ["det_coeff"]
freeze = {}
freeze["det_coeff"] = np.zeros(wf.parameters["det_coeff"].shape).astype(bool)
freeze["det_coeff"][0] = True # Determinant coefficient pivot
for s, k in enumerate(['mo_coeff_alpha','mo_coeff_beta']):
to_freeze = np.zeros(wf.parameters[k].shape, dtype=bool)
to_freeze[:, freeze_orb[s]] = True
if to_freeze.sum() < np.prod(to_freeze.shape):
freeze[k] = to_freeze
to_opt.append(k)
return wf, to_opt, freeze
def test():
"""
Tests that the multi-slater wave function value, gradient and
parameter gradient evaluations are working correctly. Also
checks that VMC energy matches energy calculated in PySCF
"""
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5",
basis="cc-pvtz",
unit="bohr",
spin=0)
epsilon = 1e-4
delta = 1e-5
nsteps = 200
warmup = 10
for mf in [scf.RHF(mol).run(), scf.ROHF(mol).run(), scf.UHF(mol).run()]:
# Test same number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(1, 1))
mc.kernel()
wf = MultiSlater(mol, mf, mc)
nconf = 10
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Test same number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(1, 1))
mc.kernel()
wf = pyqmc.default_msj(mol, mf, mc)[0]
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Test different number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(2, 0))
mc.kernel()
wf = MultiSlater(mol, mf, mc)
nelec = np.sum(mol.nelec)
epos = initial_guess(mol, nconf)
for k, item in testwf.test_updateinternals(wf, epos).items():
assert item < epsilon
assert testwf.test_wf_gradient(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta)[0] < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta)[0] < epsilon
# Quick VMC test
nconf = 1000
coords = initial_guess(mol, nconf)
df, coords = vmc(wf,
coords,
nsteps=nsteps,
accumulators={"energy": EnergyAccumulator(mol)})
df = pd.DataFrame(df)
df = reblock(df["energytotal"][warmup:], 20)
en = df.mean()
err = df.sem()
assert en - mc.e_tot < 5 * err