def init_amplitudes(self, ham):
"""
Initialize amplitudes from interaction
"""
e_ai = cc_denom(ham.f, 2, 'dir', 'full')
e_abij = cc_denom(ham.f, 4, 'dir', 'full')
nocc = self.mos.nocc
nvir = self.mos.nvir
t1 = ham.f.ov.transpose().conj() * (- e_ai)
v_vovo = einsum("pia,pjb->aibj", ham.l.pov, ham.l.pov).conj()
t2_full = v_vovo.transpose([0, 2, 1, 3]) * (- e_abij)
t2names = ['xlam', 'x1', 'x2', 'x3', 'x4']
t3names = ['xlam', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']
t2x = ncpd_initialize(t2_full.shape, self.rankt.t2)
t2x = als_dense(t2x, t2_full, max_cycle=100,
tensor_format='ncpd')
t3x = ncpd_initialize(
(nvir,) * 3 + (nocc,) * 3,
self.rankt.t3,
init_function=(lambda x: 0.001 * np.random.rand(*x)))
return Tensors(t1=t1, t2=Tensors(zip(t2names, t2x)),
t3=Tensors(zip(t3names, t3x)))
def init_amplitudes(self, ham):
"""
Initialize amplitudes from interaction
"""
e_ai = cc_denom(ham.f, 2, 'dir', 'full')
e_abij = cc_denom(ham.f, 4, 'dir', 'full')
t1 = ham.f.ov.transpose().conj() * (-e_ai)
v_vovo = einsum("pia,pjb->aibj", ham.l.pov, ham.l.pov).conj()
t2_full = v_vovo.transpose([0, 2, 1, 3]) * (-e_abij)
xs = ncpd_initialize(t2_full.shape, self.rankt.t2)
xs = als_dense(xs, t2_full, max_cycle=100, tensor_format='ncpd')
# xs_sym = cpd_symmetrize(xs, {(1, 0, 3, 2): ('ident',)})
names = ['xlam', 'x1', 'x2', 'x3', 'x4']
return Tensors(t1=t1, t2=Tensors(zip(names, xs)))
def run_cc_cpd(workdir):
from pyscf.lib import logger
print('Running CC-CPD')
# Restore RHF object
with open(workdir + 'rhf_results.p', 'rb') as fp:
rhf_results = pickle.load(fp)
e_tot, mo_coeff, mo_energy, atom = rhf_results
mol = gto.Mole()
mol.atom = atom
mol.basis = BASIS
mol.build()
# nbasis = mol.nao_nr()
rhf = scf.density_fit(scf.RHF(mol))
rhf.max_cycle = 1
rhf.scf()
nbasis_ri = rhf.with_df.get_naoaux()
# Get CCSD results to generate initial guess
with open(workdir + 'ccsd_results.p', 'rb') as fp:
energy_ref, amps_ref = pickle.load(fp)
# Run RCCSD_RI_CPD
from tcc.rccsd_cpd import RCCSD_nCPD_LS_T
from tcc.cc_solvers import classic_solver, update_diis_solver
from tcc.tensors import Tensors
from tcc.cpd import ncpd_initialize, als_dense
ranks_t = [int(el * nbasis_ri) for el in RANKS_T_FACTOR]
energies = []
deltas = []
for idxr, rank in enumerate(ranks_t):
tim = time.process_time()
if not isfile(
workdir +
'ccsd_results_rank_{}.p'.format(rank)):
cc = RCCSD_nCPD_LS_T(rhf,
mo_coeff=mo_coeff,
mo_energy=mo_energy)
# Initial guess
t2x = als_dense(
ncpd_initialize(amps_ref.t2.shape, rank),
amps_ref.t2, max_cycle=100, tensor_format='ncpd'
)
t2names = ['xlam', 'x1', 'x2', 'x3', 'x4']
amps_guess = Tensors(t1=amps_ref.t1,
t2=Tensors(zip(t2names, t2x)))
converged, energy, amps = update_diis_solver(
cc, conv_tol_energy=1e-6, conv_tol_res=1e-6,
max_cycle=500,
verbose=logger.INFO,
amps=amps_guess, ndiis=3)
if not converged:
energy = np.nan
ccsd_results = [energy, amps]
with open(workdir +
'ccsd_results_rank_{}.p'.format(rank), 'wb') as fp:
pickle.dump(ccsd_results, fp)
else:
with open(workdir +
'ccsd_results_rank_{}.p'.format(rank), 'rb') as fp:
ccsd_results = pickle.load(fp)
energy, _ = ccsd_results
energies.append(energy)
deltas.append(energy - energy_ref)
elapsed = time.process_time() - tim
print('Step {} out of {} done, rank = {}, time: {}'.format(
idxr + 1, len(ranks_t), rank, elapsed
))
np.savetxt(
workdir + 'RCCSD_CPD.txt',
np.column_stack((ranks_t, energies, deltas)),
header='Rank Energy Delta',
fmt=('%i', '%e', '%e')
)
print('Summary')
print('=========================================')
for idxr, rank in enumerate(ranks_t):
print('{} {}'.format(rank, deltas[idxr]))
print('=========================================')