def test_pbc_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf.pbc import lib, gto, scf
from pyqmc.supercell import get_supercell
from pyqmc.slater import Slater
from pyqmc.multiplywf import MultiplyWF
from pyqmc import default_jastrow
import pyqmc
mol = gto.M(
atom="H 0. 0. 0.; H 1. 1. 1.",
basis="sto-3g",
unit="bohr",
a=(np.ones((3, 3)) - np.eye(3)) * 4,
)
mf = scf.KRKS(mol, mol.make_kpts((2, 2, 2))).run()
# mf_rohf = scf.KROKS(mol).run()
# mf_uhf = scf.KUKS(mol).run()
epsilon = 1e-5
nconf = 10
supercell = get_supercell(mol, S=(np.ones((3, 3)) - 2 * np.eye(3)))
epos = pyqmc.initial_guess(supercell, nconf)
# For multislaterpbc
# kinds = 0, 3, 5, 6 # G, X, Y, Z
# d1 = {kind: [0] for kind in kinds}
# d2 = d1.copy()
# d2.update({0: [], 3: [0, 1]})
# detwt = [2 ** 0.5, 2 ** 0.5]
# occup = [[d1, d2], [d1]]
# map_dets = [[0, 1], [0, 0]]
for wf in [
MultiplyWF(Slater(supercell, mf), default_jastrow(supercell)[0]),
Slater(supercell, mf),
]:
for k in wf.parameters:
if "mo_coeff" not in k and k != "det_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
_, epos = pyqmc.vmc(wf, epos, nblocks=1, nsteps=2, tstep=1) # move off node
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta))
print(type(wf), fname, min(err))
assert min(err) < epsilon
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
def test_vmc():
"""
Test that a VMC calculation of a Slater determinant matches Hartree-Fock within error bars.
"""
nconf = 1000
mol = gto.M(atom="Li 0. 0. 0.; Li 0. 0. 1.5",
basis="cc-pvtz",
unit="bohr",
verbose=1)
mf_rhf = scf.RHF(mol).run()
mf_uhf = scf.UHF(mol).run()
nsteps = 300
warmup = 30
for wf, mf in [
(Slater(mol, mf_rhf), mf_rhf),
(Slater(mol, mf_uhf), mf_uhf),
]:
# Without blocks
coords = initial_guess(mol, nconf)
df, coords = vmc(
wf,
coords,
nsteps=nsteps,
accumulators={"energy": EnergyAccumulator(mol)},
verbose=True,
)
df = pd.DataFrame(df)
df = reblock(df["energytotal"][warmup:], 20)
en = df.mean()
err = df.sem()
assert en - mf.energy_tot(
) < 5 * err, "pyscf {0}, vmc {1}, err {2}".format(
mf.energy_tot(), en, err)
# With blocks
coords = initial_guess(mol, nconf)
df, coords = vmc(
wf,
coords,
nblocks=int(nsteps / 30),
nsteps_per_block=30,
accumulators={"energy": EnergyAccumulator(mol)},
)
df = pd.DataFrame(df)["energytotal"][int(warmup / 30):]
en = df.mean()
err = df.sem()
assert en - mf.energy_tot(
) < 5 * err, "pyscf {0}, vmc {1}, err {2}".format(
mf.energy_tot(), en, err)
def test_wfs():
"""
Ensure that the wave function objects are consistent in several situations.
"""
from pyscf import lib, gto, scf
from pyqmc import Slater
from pyqmc.multiplywf import MultiplyWF
from pyqmc.manybody_jastrow import J3
from pyqmc import default_jastrow
import pyqmc
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5", basis="sto-3g", unit="bohr")
mf = scf.RHF(mol).run()
mf_rohf = scf.ROHF(mol).run()
mf_uhf = scf.UHF(mol).run()
epsilon = 1e-5
nconf = 10
epos = pyqmc.initial_guess(mol, nconf)
for wf in [
default_jastrow(mol)[0],
J3(mol),
MultiplyWF(Slater(mol, mf), default_jastrow(mol)[0]),
MultiplyWF(Slater(mol, mf), default_jastrow(mol)[0], J3(mol)),
Slater(mol, mf_uhf),
Slater(mol, mf),
Slater(mol, mf_rohf),
]:
for k in wf.parameters:
if k != "mo_coeff":
wf.parameters[k] = np.random.rand(*wf.parameters[k].shape)
for k, item in testwf.test_updateinternals(wf, epos).items():
print(k, item)
assert item < epsilon
testwf.test_mask(wf, 0, epos)
_, epos = pyqmc.vmc(wf, epos, nblocks=1, nsteps=2, tstep=1) # move off node
for fname, func in zip(
["gradient", "laplacian", "pgradient"],
[
testwf.test_wf_gradient,
testwf.test_wf_laplacian,
testwf.test_wf_pgradient,
],
):
err = []
for delta in [1e-4, 1e-5, 1e-6, 1e-7, 1e-8]:
err.append(func(wf, epos, delta))
print(type(wf), fname, min(err))
assert min(err) < epsilon, "epsilon {0}".format(epsilon)
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 [
Slater(mol, mf),
default_jastrow(mol)[0],
MultiplyWF(Slater(mol, mf),
default_jastrow(mol)[0]),
]:
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 [Slater(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 test():
mol = gto.M(atom="Li 0. 0. 0.; Li 0. 0. 1.5",
basis="sto-3g",
unit="bohr",
verbose=0)
mf = scf.RHF(mol).run()
# Lowdin orthogonalized AO basis.
lowdin = lo.orth_ao(mol, "lowdin")
# MOs in the Lowdin basis.
mo = solve(lowdin, mf.mo_coeff)
# make AO to localized orbital coefficients.
mfobdm = mf.make_rdm1(mo, mf.mo_occ)
### Test OBDM calculation.
nconf = 500
nsteps = 400
warmup = 15
wf = Slater(mol, mf)
configs = initial_guess(mol, nconf)
obdm_dict = dict(mol=mol, orb_coeff=lowdin, nsweeps=5, warmup=15)
obdm = OBDMAccumulator(**obdm_dict)
obdm_up = OBDMAccumulator(**obdm_dict, spin=0)
obdm_down = OBDMAccumulator(**obdm_dict, spin=1)
df, coords = vmc(
wf,
configs,
nsteps=nsteps,
accumulators={
"obdm": obdm,
"obdm_up": obdm_up,
"obdm_down": obdm_down
},
)
obdm_est = {}
for k in ["obdm", "obdm_up", "obdm_down"]:
avg_norm = np.mean(df[k + "norm"][warmup:], axis=0)
avg_obdm = np.mean(df[k + "value"][warmup:], axis=0)
obdm_est[k] = normalize_obdm(avg_obdm, avg_norm)
print("Average OBDM(orb,orb)", obdm_est["obdm"].diagonal().round(3))
print("mf obdm", mfobdm.diagonal().round(3))
assert np.max(np.abs(obdm_est["obdm"] - mfobdm)) < 0.05
print(obdm_est["obdm_up"].diagonal().round(3))
print(obdm_est["obdm_down"].diagonal().round(3))
assert np.mean(
np.abs(obdm_est["obdm_up"] + obdm_est["obdm_down"] - mfobdm)) < 0.05
def test_accumulator():
"""Tests that the accumulator gets inserted into the data output correctly."""
mol = gto.M(atom="Li 0. 0. 0.; Li 0. 0. 1.5",
basis="cc-pvtz",
unit="bohr",
verbose=5)
mf = scf.RHF(mol).run()
nconf = 5000
wf = Slater(mol, mf)
coords = initial_guess(mol, nconf)
df, coords = vmc(wf,
coords,
nsteps=30,
accumulators={"energy": EnergyAccumulator(mol)})
df = pd.DataFrame(df)
eaccum = EnergyAccumulator(mol)
eaccum_energy = eaccum(coords, wf)
df = pd.DataFrame(df)
print(df["energytotal"][29] == np.average(eaccum_energy["total"]))
assert df["energytotal"][29] == np.average(eaccum_energy["total"])
def test_ecp():
mol = gto.M(atom="C 0. 0. 0.", ecp="bfd", basis="bfd_vtz")
mf = scf.RHF(mol).run()
nconf = 5000
wf = Slater(mol, mf)
coords = initial_guess(mol, nconf)
df, coords = vmc(wf,
coords,
nsteps=100,
accumulators={"energy": EnergyAccumulator(mol)})
df = pd.DataFrame(df)
warmup = 30
print(
"mean field",
mf.energy_tot(),
"vmc estimation",
np.mean(df["energytotal"][warmup:]),
np.std(df["energytotal"][warmup:]),
)
assert abs(mf.energy_tot() -
np.mean(df["energytotal"][warmup:])) <= np.std(
df["energytotal"][warmup:])
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.UHF(mol).run()
]: # [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 = Slater(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) < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta) < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta) < 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) < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta) < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta) < epsilon
# Test different number of elecs
mc = mcscf.CASCI(mf, ncas=4, nelecas=(2, 0))
mc.kernel()
wf = Slater(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) < epsilon
assert testwf.test_wf_laplacian(wf, epos, delta=delta) < epsilon
assert testwf.test_wf_pgradient(wf, epos, delta=delta) < 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
def test(atom="He", total_spin=0, total_charge=0, scf_basis="sto-3g"):
mol = gto.M(
atom="%s 0. 0. 0.; %s 0. 0. 1.5" % (atom, atom),
basis=scf_basis,
unit="bohr",
verbose=4,
spin=total_spin,
charge=total_charge,
)
mf = scf.UHF(mol).run()
# Intrinsic Atomic Orbitals
iaos = make_separate_spin_iaos(mol,
mf,
np.array([i for i in range(mol.natm)]),
iao_basis="minao")
# iaos=make_combined_spin_iaos(mol,mf,np.array([i for i in range(mol.natm)]),iao_basis='minao')
# MOs in the IAO basis
mo = reps_combined_spin_iaos(
iaos,
mf,
np.einsum("i,j->ji", np.arange(mf.mo_coeff[0].shape[1]),
np.array([1, 1])),
)
# Mean-field obdm in IAO basis
mfobdm = mf.make_rdm1(mo, mf.mo_occ)
# Mean-field tbdm in IAO basis
mftbdm = singledet_tbdm(mf, mfobdm)
### Test TBDM calculation.
# VMC params
nconf = 500
n_vmc_steps = 400
vmc_tstep = 0.3
vmc_warmup = 30
# TBDM params
tbdm_sweeps = 4
tbdm_tstep = 0.5
wf = Slater(mol, mf) # Single-Slater (no jastrow) wf
configs = initial_guess(mol, nconf)
energy = EnergyAccumulator(mol)
obdm_up = OBDMAccumulator(mol=mol,
orb_coeff=iaos[0],
nsweeps=tbdm_sweeps,
spin=0)
obdm_down = OBDMAccumulator(mol=mol,
orb_coeff=iaos[1],
nsweeps=tbdm_sweeps,
spin=1)
tbdm_upup = TBDMAccumulator(mol=mol,
orb_coeff=iaos,
nsweeps=tbdm_sweeps,
tstep=tbdm_tstep,
spin=(0, 0))
tbdm_updown = TBDMAccumulator(mol=mol,
orb_coeff=iaos,
nsweeps=tbdm_sweeps,
tstep=tbdm_tstep,
spin=(0, 1))
tbdm_downup = TBDMAccumulator(mol=mol,
orb_coeff=iaos,
nsweeps=tbdm_sweeps,
tstep=tbdm_tstep,
spin=(1, 0))
tbdm_downdown = TBDMAccumulator(mol=mol,
orb_coeff=iaos,
nsweeps=tbdm_sweeps,
tstep=tbdm_tstep,
spin=(1, 1))
print("VMC...")
df, coords = vmc(
wf,
configs,
nsteps=n_vmc_steps,
tstep=vmc_tstep,
accumulators={
"energy": energy,
"obdm_up": obdm_up,
"obdm_down": obdm_down,
"tbdm_upup": tbdm_upup,
"tbdm_updown": tbdm_updown,
"tbdm_downup": tbdm_downup,
"tbdm_downdown": tbdm_downdown,
},
verbose=True,
)
# Compares obdm from QMC and MF
obdm_est = {}
for k in ["obdm_up", "obdm_down"]:
avg_norm = np.mean(df[k + "norm"][vmc_warmup:], axis=0)
avg_obdm = np.mean(df[k + "value"][vmc_warmup:], axis=0)
obdm_est[k] = normalize_obdm(avg_obdm, avg_norm)
qmcobdm = np.array([obdm_est["obdm_up"], obdm_est["obdm_down"]])
print("\nComparing QMC and MF obdm:")
for s in [0, 1]:
# print('QMC obdm[%d]:\n'%s,qmcobdm[s])
# print('MF obdm[%d]:\n'%s,mfobdm[s])
print("diff[%d]:\n" % s, qmcobdm[s] - mfobdm[s])
# Compares tbdm from QMC and MF
avg_norm = {}
avg_tbdm = {}
for t in ["tbdm_upup", "tbdm_updown", "tbdm_downup", "tbdm_downdown"]:
for k in df.keys():
if k.startswith(t + "norm_"):
avg_norm[k.split("_")[-1]] = np.mean(df[k][vmc_warmup:],
axis=0)
if k.startswith(t + "value"):
avg_tbdm[k.split("_")[-1]] = np.mean(df[k][vmc_warmup:],
axis=0)
tbdm_est = {
k: normalize_tbdm(avg_tbdm[k].reshape(2, 2, 2, 2), avg_norm["a"],
avg_norm["b"])
for k in avg_tbdm
}
qmctbdm = np.array([
[tbdm_est["upupvalue"], tbdm_est["updownvalue"]],
[tbdm_est["downupvalue"], tbdm_est["downdownvalue"]],
])
print("\nComparing QMC and MF tbdm:")
for sa, sb in [[0, 0], [0, 1], [1, 0], [1, 1]]:
print("spins", sa, sb)
print("QMC tbdm[%d,%d]:\n" % (sa, sb), qmctbdm[sa, sb].round(3))
print("MF tbdm[%d,%d]:\n" % (sa, sb), mftbdm[sa, sb].round(3))
diff = qmctbdm[sa, sb] - mftbdm[sa, sb]
print("diff[%d,%d]:\n" % (sa, sb), diff)
assert np.max(np.abs(diff)) < 0.05
def test_pbc():
from pyscf.pbc import gto, scf
from pyqmc import supercell
import scipy
lvecs = (np.ones((3, 3)) - 2 * np.eye(3)) * 2.0
mol = gto.M(
atom="H 0. 0. -{0}; H 0. 0. {0}".format(0.7),
basis="sto-3g",
unit="bohr",
verbose=0,
a=lvecs,
)
mf = scf.KRHF(mol, kpts=mol.make_kpts((2, 2, 2)))
mf = mf.run()
S = np.ones((3, 3)) - np.eye(3)
mol = supercell.get_supercell(mol, S)
kpts = supercell.get_supercell_kpts(mol)[:2]
kdiffs = mf.kpts[np.newaxis] - kpts[:, np.newaxis]
kinds = np.nonzero(np.linalg.norm(kdiffs, axis=-1) < 1e-12)[1]
# Lowdin orthogonalized AO basis.
# lowdin = lo.orth_ao(mol, "lowdin")
loiao = lo.iao.iao(mol.original_cell, mf.mo_coeff, kpts=kpts)
occs = [mf.mo_occ[k] for k in kinds]
coefs = [mf.mo_coeff[k] for k in kinds]
ovlp = mf.get_ovlp()[kinds]
lowdin = [lo.vec_lowdin(l, o) for l, o in zip(loiao, ovlp)]
lreps = [
np.linalg.multi_dot([l.T, o, c])
for l, o, c in zip(lowdin, ovlp, coefs)
]
# make AO to localized orbital coefficients.
mfobdm = [
np.einsum("ij,j,kj->ik", l.conj(), o, l) for l, o in zip(lreps, occs)
]
### Test OBDM calculation.
nconf = 800
nsteps = 50
warmup = 6
wf = Slater(mol, mf)
configs = initial_guess(mol, nconf)
obdm_dict = dict(mol=mol,
orb_coeff=lowdin,
kpts=kpts,
nsweeps=4,
warmup=10)
obdm = OBDMAccumulator(**obdm_dict)
obdm_up = OBDMAccumulator(**obdm_dict, spin=0)
obdm_down = OBDMAccumulator(**obdm_dict, spin=1)
df, coords = vmc(
wf,
configs,
nsteps=nsteps,
accumulators={
"obdm": obdm,
"obdm_up": obdm_up,
"obdm_down": obdm_down
},
verbose=True,
)
obdm_est = {}
for k in ["obdm", "obdm_up", "obdm_down"]:
avg_norm = np.mean(df[k + "norm"][warmup:], axis=0)
avg_obdm = np.mean(df[k + "value"][warmup:], axis=0)
obdm_est[k] = normalize_obdm(avg_obdm, avg_norm)
print("Average OBDM(orb,orb)", obdm_est["obdm"].round(3))
mfobdm = scipy.linalg.block_diag(*mfobdm)
print("mf obdm", mfobdm.round(3))
max_abs_err = np.max(np.abs(obdm_est["obdm"] - mfobdm))
assert max_abs_err < 0.05, "max abs err {0}".format(max_abs_err)
print(obdm_est["obdm_up"].diagonal().round(3))
print(obdm_est["obdm_down"].diagonal().round(3))
mae = np.mean(np.abs(obdm_est["obdm_up"] + obdm_est["obdm_down"] - mfobdm))
maup = np.mean(np.abs(obdm_est["obdm_up"]))
madn = np.mean(np.abs(obdm_est["obdm_down"]))
mamf = np.mean(np.abs(mfobdm))
assert mae < 0.05, "mae {0}\n maup {1}\n madn {2}\n mamf {3}".format(
mae, maup, madn, mamf)
def test():
""" Ensure that DMC obtains the exact result for a hydrogen atom """
from pyscf import gto, scf
from pyqmc.jastrowspin import JastrowSpin
from pyqmc.dmc import limdrift, rundmc
from pyqmc.mc import vmc
from pyqmc.accumulators import EnergyAccumulator
from pyqmc.func3d import CutoffCuspFunction
from pyqmc.multiplywf import MultiplyWF
from pyqmc.coord import OpenConfigs
from pyqmc import Slater
import pandas as pd
mol = gto.M(atom="H 0. 0. 0.", basis="sto-3g", unit="bohr", spin=1)
mf = scf.UHF(mol).run()
nconf = 1000
configs = OpenConfigs(np.random.randn(nconf, 1, 3))
wf1 = Slater(mol, mf)
wf = wf1
wf2 = JastrowSpin(mol, a_basis=[CutoffCuspFunction(5, 0.2)], b_basis=[])
wf2.parameters["acoeff"] = np.asarray([[[1.0, 0]]])
wf = MultiplyWF(wf1, wf2)
dfvmc, configs_ = vmc(wf,
configs,
nsteps=50,
accumulators={"energy": EnergyAccumulator(mol)})
dfvmc = pd.DataFrame(dfvmc)
print(
"vmc energy",
np.mean(dfvmc["energytotal"]),
np.std(dfvmc["energytotal"]) / np.sqrt(len(dfvmc)),
)
warmup = 200
branchtime = 5
dfdmc, configs_, weights_ = rundmc(
wf,
configs,
nsteps=4000 + warmup * branchtime,
branchtime=branchtime,
accumulators={"energy": EnergyAccumulator(mol)},
ekey=("energy", "total"),
tstep=0.01,
drift_limiter=limdrift,
verbose=True,
)
dfdmc = pd.DataFrame(dfdmc)
dfdmc.sort_values("step", inplace=True)
dfprod = dfdmc[dfdmc.step >= warmup]
rb_summary = reblock.reblock_summary(dfprod[["energytotal", "energyei"]],
20)
print(rb_summary)
energy, err = [
rb_summary[v]["energytotal"] for v in ("mean", "standard error")
]
assert (np.abs(energy + 0.5) <
5 * err), "energy not within {0} of -0.5: energy {1}".format(
5 * err, np.mean(energy))