def make_modchg_basis(auxcell, smooth_eta, l_max=3):
# * chgcell defines smooth gaussian functions for each angular momentum for
# auxcell. The smooth functions may be used to carry the charge
chgcell = copy.copy(
auxcell) # smooth model density for coulomb integral to carry charge
half_sph_norm = .5 / numpy.sqrt(numpy.pi)
chg_bas = []
chg_env = [smooth_eta]
ptr_eta = auxcell._env.size
ptr = ptr_eta + 1
for ia in range(auxcell.natm):
for l in set(auxcell._bas[auxcell._bas[:, gto.ATOM_OF] == ia,
gto.ANG_OF]):
if l <= l_max:
norm = half_sph_norm / gto.mole._gaussian_int(
l * 2 + 2, smooth_eta)
chg_bas.append([ia, l, 1, 1, 0, ptr_eta, ptr, 0])
chg_env.append(norm)
ptr += 1
chgcell._atm = auxcell._atm
chgcell._bas = numpy.asarray(chg_bas,
dtype=numpy.int32).reshape(-1, gto.BAS_SLOTS)
chgcell._env = numpy.hstack((auxcell._env, chg_env))
chgcell._built = True
logger.debug1(auxcell,
'make smooth basis, num shells = %d, num cGTOs = %d',
chgcell.nbas, chgcell.nao_nr())
return chgcell
def dump_flags(self):
log = logger.Logger(self.stdout, self.verbose)
logger.info(self, '\n')
logger.info(self, '******** %s flags ********', self.__class__)
logger.info(self, 'gs = %s', self.gs)
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
def update(self, s, d, f, *args, **kwargs):
errvec = get_err_vec(s, d, f)
logger.debug1(self, 'diis-norm(errvec)=%g', numpy.linalg.norm(errvec))
xnew = lib.diis.DIIS.update(self, f, xerr=errvec)
if self.rollback > 0 and len(self._bookkeep) == self.space:
self._bookkeep = self._bookkeep[-self.rollback:]
return xnew
def unpackE4_BLOCK(self, fname, norb):
# The 4RDMs written by "Fourpdm_container::save_spatial_npdm_binary" in BLOCK
# are written as E4[i1,j2,k3,l4,m4,n3,o2,p1]
# and are stored here as E4[i1,j2,k3,l4,p1,o2,n3,m4]
# This is done with SQA in mind.
E4 = numpy.zeros((norb, norb, norb, norb, norb, norb, norb, norb),
order='F')
fil = open(fname, "rb")
logger.debug1(
self,
"[fil.seek(not_really_understood)]: HOW DANGEROUS IS THAT ???!?!?!?"
)
fil.seek(109) # HOW DANGEROUS IS THAT ???!?!?!?
for a in range(norb):
for b in range(norb):
for c in range(norb):
for d in range(norb):
for e in range(norb):
for f in range(norb):
for g in range(norb):
for h in range(norb):
(value, ) = struct.unpack(
'd', fil.read(8))
E4[a, b, c, d, h, g, f, e] = value
try:
(value, ) = struct.unpack('c', fil.read(1))
logger.warn(self, "MORE bytes TO READ!")
except:
logger.warn(self, "AT LEAST, NO MORE bytes TO READ!")
#exit(0)
fil.close()
return E4
def make_modchg_basis(auxcell, smooth_eta):
# * chgcell defines smooth gaussian functions for each angular momentum for
# auxcell. The smooth functions may be used to carry the charge
chgcell = copy.copy(auxcell) # smooth model density for coulomb integral to carry charge
half_sph_norm = .5/numpy.sqrt(numpy.pi)
chg_bas = []
chg_env = [smooth_eta]
ptr_eta = auxcell._env.size
ptr = ptr_eta + 1
l_max = auxcell._bas[:,gto.ANG_OF].max()
# gaussian_int(l*2+2) for multipole integral:
# \int (r^l e^{-ar^2} * Y_{lm}) (r^l Y_{lm}) r^2 dr d\Omega
norms = [half_sph_norm/gto.gaussian_int(l*2+2, smooth_eta)
for l in range(l_max+1)]
for ia in range(auxcell.natm):
for l in set(auxcell._bas[auxcell._bas[:,gto.ATOM_OF]==ia, gto.ANG_OF]):
chg_bas.append([ia, l, 1, 1, 0, ptr_eta, ptr, 0])
chg_env.append(norms[l])
ptr += 1
chgcell._atm = auxcell._atm
chgcell._bas = numpy.asarray(chg_bas, dtype=numpy.int32).reshape(-1,gto.BAS_SLOTS)
chgcell._env = numpy.hstack((auxcell._env, chg_env))
chgcell.rcut = _estimate_rcut(smooth_eta, l_max, 1., auxcell.precision)
logger.debug1(auxcell, 'make compensating basis, num shells = %d, num cGTOs = %d',
chgcell.nbas, chgcell.nao_nr())
logger.debug1(auxcell, 'chgcell.rcut %s', chgcell.rcut)
return chgcell
def jk_method(self, J='FFTDF', K=None):
'''
Set up the schemes to evaluate Coulomb and exchange matrix
FFTDF: planewave density fitting using Fast Fourier Transform
AFTDF: planewave density fitting using analytic Fourier Transform
GDF: Gaussian density fitting
MDF: Gaussian and planewave mix density fitting
RS: range-separation JK builder
RSDF: range-separation density fitting
'''
if K is None:
K = J
if J != K:
raise NotImplementedError('J != K')
if 'DF' in J or 'DF' in K:
if 'DF' in J and 'DF' in K:
assert J == K
else:
df_method = J if 'DF' in J else K
self.with_df = getattr(df, df_method)(self.cell, self.kpts)
if 'RS' in J or 'RS' in K:
self.rsjk = RangeSeparationJKBuilder(self.cell, self.kpts)
self.rsjk.verbose = self.verbose
# For nuclear attraction
if J == 'RS' and K == 'RS' and not isinstance(self.with_df, df.GDF):
self.with_df = df.GDF(self.cell, self.kpts)
nuc = self.with_df.__class__.__name__
logger.debug1(self, 'Apply %s for J, %s for K, %s for nuc', J, K, nuc)
return self
def unpackE3_BLOCK(self, fname, norb):
# The 3RDMs written by "Threepdm_container::save_spatial_npdm_binary" in BLOCK
# are written as E3[i1,j2,k3,l3,m2,n1]
# and are stored here as E3[i1,j2,k3,n1,m2,l3]
# This is done with SQA in mind.
E3 = numpy.zeros((norb, norb, norb, norb, norb, norb), order='F')
fil = open(fname, "rb")
logger.debug1(
self,
"[fil.seek(not_really_understood)]: HOW DANGEROUS IS THAT ???!?!?!?"
)
#fil.seek(93) # HOW DANGEROUS IS THAT ???!?!?!?
fil.seek(53) # HOW DANGEROUS IS THAT ???!?!?!?
for a in range(norb):
for b in range(norb):
for c in range(norb):
for d in range(norb):
for e in range(norb):
for f in range(norb):
(value, ) = struct.unpack('d', fil.read(8))
E3[a, b, c, f, e, d] = value
try:
(value, ) = struct.unpack('c', fil.read(1))
logger.warn(self, "MORE bytes TO READ!")
except:
logger.warn(self, "AT LEAST, NO MORE bytes TO READ!")
#exit(0)
fil.close()
return E3
def get_occ(mf, mo_energy=None, mo_coeff=None):
'''Label the occupancies for each orbital.
NOTE the occupancies are not assigned based on the orbital energy ordering.
The first N orbitals are assigned to be occupied orbitals.
Examples:
>>> mol = gto.M(atom='H 0 0 0; O 0 0 1.1', spin=1)
>>> mf = scf.hf.SCF(mol)
>>> energy = numpy.array([-10., -1., 1, -2., 0, -3])
>>> mf.get_occ(energy)
array([2, 2, 2, 2, 1, 0])
'''
if mo_energy is None: mo_energy = mf.mo_energy
if getattr(mo_energy, 'mo_ea', None) is not None:
mo_ea = mo_energy.mo_ea
mo_eb = mo_energy.mo_eb
else:
mo_ea = mo_eb = mo_energy
nmo = mo_ea.size
mo_occ = numpy.zeros(nmo)
if getattr(mf, 'nelec', None) is None:
nelec = mf.mol.nelec
else:
nelec = mf.nelec
ncore = nelec[1]
nocc = nelec[0]
nopen = abs(nocc - ncore)
mo_occ = _fill_rohf_occ(mo_energy, mo_ea, mo_eb, ncore, nopen)
if mf.verbose >= logger.INFO and nocc < nmo and ncore > 0:
ehomo = max(mo_energy[mo_occ> 0])
elumo = min(mo_energy[mo_occ==0])
if ehomo+1e-3 > elumo:
logger.warn(mf, 'H**O %.15g >= LUMO %.15g', ehomo, elumo)
else:
logger.info(mf, ' H**O = %.15g LUMO = %.15g', ehomo, elumo)
if nopen > 0 and mf.verbose >= logger.DEBUG:
core_idx = mo_occ == 2
open_idx = mo_occ == 1
vir_idx = mo_occ == 0
logger.debug(mf, ' Roothaan | alpha | beta')
logger.debug(mf, ' Highest 2-occ = %18.15g | %18.15g | %18.15g',
max(mo_energy[core_idx]),
max(mo_ea[core_idx]), max(mo_eb[core_idx]))
logger.debug(mf, ' Lowest 0-occ = %18.15g | %18.15g | %18.15g',
min(mo_energy[vir_idx]),
min(mo_ea[vir_idx]), min(mo_eb[vir_idx]))
for i in numpy.where(open_idx)[0]:
logger.debug(mf, ' 1-occ = %18.15g | %18.15g | %18.15g',
mo_energy[i], mo_ea[i], mo_eb[i])
if mf.verbose >= logger.DEBUG:
numpy.set_printoptions(threshold=nmo)
logger.debug(mf, ' Roothaan mo_energy =\n%s', mo_energy)
logger.debug1(mf, ' alpha mo_energy =\n%s', mo_ea)
logger.debug1(mf, ' beta mo_energy =\n%s', mo_eb)
numpy.set_printoptions(threshold=1000)
return mo_occ
def get_occ(mf, mo_energy=None, mo_coeff=None):
'''Label the occupancies for each orbital.
NOTE the occupancies are not assigned based on the orbital energy ordering.
The first N orbitals are assigned to be occupied orbitals.
Examples:
>>> mol = gto.M(atom='H 0 0 0; O 0 0 1.1', spin=1)
>>> mf = scf.hf.SCF(mol)
>>> energy = numpy.array([-10., -1., 1, -2., 0, -3])
>>> mf.get_occ(energy)
array([2, 2, 2, 2, 1, 0])
'''
if mo_energy is None: mo_energy = mf.mo_energy
if getattr(mo_energy, 'mo_ea', None) is not None:
mo_ea = mo_energy.mo_ea
mo_eb = mo_energy.mo_eb
else:
mo_ea = mo_eb = mo_energy
nmo = mo_ea.size
mo_occ = numpy.zeros(nmo)
if getattr(mf, 'nelec', None) is None:
nelec = mf.mol.nelec
else:
nelec = mf.nelec
ncore = nelec[1]
nocc = nelec[0]
nopen = abs(nocc - ncore)
mo_occ = _fill_rohf_occ(mo_energy, mo_ea, mo_eb, ncore, nopen)
if mf.verbose >= logger.INFO and nocc < nmo and ncore > 0:
ehomo = max(mo_energy[mo_occ> 0])
elumo = min(mo_energy[mo_occ==0])
if ehomo+1e-3 > elumo:
logger.warn(mf, 'H**O %.15g >= LUMO %.15g', ehomo, elumo)
else:
logger.info(mf, ' H**O = %.15g LUMO = %.15g', ehomo, elumo)
if nopen > 0 and mf.verbose >= logger.DEBUG:
core_idx = mo_occ == 2
open_idx = mo_occ == 1
vir_idx = mo_occ == 0
logger.debug(mf, ' Roothaan | alpha | beta')
logger.debug(mf, ' Highest 2-occ = %18.15g | %18.15g | %18.15g',
max(mo_energy[core_idx]),
max(mo_ea[core_idx]), max(mo_eb[core_idx]))
logger.debug(mf, ' Lowest 0-occ = %18.15g | %18.15g | %18.15g',
min(mo_energy[vir_idx]),
min(mo_ea[vir_idx]), min(mo_eb[vir_idx]))
for i in numpy.where(open_idx)[0]:
logger.debug(mf, ' 1-occ = %18.15g | %18.15g | %18.15g',
mo_energy[i], mo_ea[i], mo_eb[i])
if mf.verbose >= logger.DEBUG:
numpy.set_printoptions(threshold=nmo)
logger.debug(mf, ' Roothaan mo_energy =\n%s', mo_energy)
logger.debug1(mf, ' alpha mo_energy =\n%s', mo_ea)
logger.debug1(mf, ' beta mo_energy =\n%s', mo_eb)
numpy.set_printoptions(threshold=1000)
return mo_occ
def update(self, s, d, f, *args, **kwargs):
errvec = get_err_vec(s, d, f)
logger.debug1(self, 'diis-norm(errvec)=%g', numpy.linalg.norm(errvec))
xnew = lib.diis.DIIS.update(self, f, xerr=errvec)
if self.rollback > 0 and len(self._bookkeep) == self.space:
self._bookkeep = self._bookkeep[-self.rollback:]
return xnew
def dump_flags(self):
log = logger.Logger(self.stdout, self.verbose)
logger.info(self, "\n")
logger.info(self, "******** %s flags ********", self.__class__)
logger.info(self, "gs = %s", self.gs)
logger.info(self, "len(kpts) = %d", len(self.kpts))
logger.debug1(self, " kpts = %s", self.kpts)
def init_guess_by_atom(mol):
'''Generate initial guess density matrix from superposition of atomic HF
density matrix. The atomic HF is occupancy averaged RHF
Returns:
Density matrix, 2D ndarray
'''
from pyscf.scf import atom_hf
atm_scf = atom_hf.get_atm_nrhf(mol)
nbf = mol.nao_nr()
dm = numpy.zeros((nbf, nbf))
p0 = 0
for ia in range(mol.natm):
symb = mol.atom_symbol(ia)
if symb in atm_scf:
e_hf, mo_e, mo_c, mo_occ = atm_scf[symb]
else:
symb = mol.atom_pure_symbol(ia)
e_hf, mo_e, mo_c, mo_occ = atm_scf[symb]
p1 = p0 + mo_e.__len__()
dm[p0:p1,p0:p1] = numpy.dot(mo_c*mo_occ, mo_c.T.conj())
p0 = p1
for k, v in atm_scf.items():
logger.debug1(mol, 'Atom %s, E = %.12g', k, v[0])
return dm
def _symmetrize_canonicalization_(mf, mo_energy, mo_coeff, s):
'''Restore symmetry for canonicalized orbitals
'''
def search_for_degeneracy(mo_energy):
idx = numpy.where(abs(mo_energy[1:] - mo_energy[:-1]) < 1e-6)[0]
return numpy.unique(numpy.hstack((idx, idx + 1)))
mol = mf.mol
degidx = search_for_degeneracy(mo_energy)
logger.debug1(mf, 'degidx %s', degidx)
if degidx.size > 0:
esub = mo_energy[degidx]
csub = mo_coeff[:, degidx]
scsub = numpy.dot(s, csub)
emin = abs(esub).min() * .5
es = []
cs = []
for i, ir in enumerate(mol.irrep_id):
so = mol.symm_orb[i]
sosc = numpy.dot(so.T, scsub)
s_ir = reduce(numpy.dot, (so.T, s, so))
fock_ir = numpy.dot(sosc * esub, sosc.T)
mo_energy, u = mf._eigh(fock_ir, s_ir)
idx = abs(mo_energy) > emin
es.append(mo_energy[idx])
cs.append(numpy.dot(mol.symm_orb[i], u[:, idx]))
es = numpy.hstack(es).round(7)
idx = numpy.argsort(es, kind='mergesort')
assert (numpy.allclose(es[idx], esub.round(7)))
mo_coeff[:, degidx] = numpy.hstack(cs)[:, idx]
return mo_coeff
def get_init_guess(self, mol=None, key='minao'):
if mol is None:
mol = self.mol
if callable(key):
dm = key(mol)
elif key.lower() == '1e':
dm = self.init_guess_by_1e(mol)
elif getattr(mol, 'natm', 0) == 0:
logger.info(self, 'No atom found in mol. Use 1e initial guess')
dm = self.init_guess_by_1e(mol)
elif key.lower() == 'atom':
dm = self.init_guess_by_atom(mol)
elif key.lower() == 'chkfile':
try:
dm = self.init_guess_by_chkfile()
except (IOError, KeyError):
logger.warn(self,
'Fail in reading %s. Use MINAO initial guess',
self.chkfile)
dm = self.init_guess_by_minao(mol)
else:
dm = self.init_guess_by_minao(mol)
if self.verbose >= logger.DEBUG1:
logger.debug1(self, 'Nelec from initial guess = %g',
(dm * self.get_ovlp()).sum().real)
return dm
def init_guess_by_atom(mol):
'''Generate initial guess density matrix from superposition of atomic HF
density matrix. The atomic HF is occupancy averaged RHF
Returns:
Density matrix, 2D ndarray
'''
from pyscf.scf import atom_hf
atm_scf = atom_hf.get_atm_nrhf(mol)
nbf = mol.nao_nr()
dm = numpy.zeros((nbf, nbf))
p0 = 0
for ia in range(mol.natm):
symb = mol.atom_symbol(ia)
if symb in atm_scf:
e_hf, mo_e, mo_c, mo_occ = atm_scf[symb]
else:
symb = mol.atom_pure_symbol(ia)
e_hf, mo_e, mo_c, mo_occ = atm_scf[symb]
p1 = p0 + mo_e.__len__()
dm[p0:p1,p0:p1] = numpy.dot(mo_c*mo_occ, mo_c.T.conj())
p0 = p1
for k, v in atm_scf.items():
logger.debug1(mol, 'Atom %s, E = %.12g', k, v[0])
return dm
def _symmetrize_canonicalization_(mol, mo_energy, mo_coeff, s):
'''Restore symmetry for canonicalized orbitals
'''
def search_for_degeneracy(mo_energy):
idx = numpy.where(abs(mo_energy[1:] - mo_energy[:-1]) < 1e-6)[0]
return numpy.unique(numpy.hstack((idx, idx+1)))
degidx = search_for_degeneracy(mo_energy)
logger.debug1(mol, 'degidx %s', degidx)
if degidx.size > 0:
esub = mo_energy[degidx]
csub = mo_coeff[:,degidx]
scsub = numpy.dot(s, csub)
emin = abs(esub).min() * .5
es = []
cs = []
for i,ir in enumerate(mol.irrep_id):
so = mol.symm_orb[i]
sosc = numpy.dot(so.T, scsub)
s_ir = reduce(numpy.dot, (so.T, s, so))
fock_ir = numpy.dot(sosc*esub, sosc.T)
mo_energy, u = scipy.linalg.eigh(fock_ir, s_ir)
idx = abs(mo_energy) > emin
es.append(mo_energy[idx])
cs.append(numpy.dot(mol.symm_orb[i], u[:,idx]))
es = numpy.hstack(es).round(9)
idx = numpy.argsort(es)
assert(numpy.allclose(es[idx], esub))
mo_coeff[:,degidx] = numpy.hstack(cs)[:,idx]
return mo_coeff
def init_guess_by_atom(mol):
'''Generate initial guess density matrix from superposition of atomic HF
density matrix. The atomic HF is occupancy averaged RHF
Returns:
Density matrix, 2D ndarray
'''
import copy
from pyscf.scf import atom_hf
from pyscf.scf import addons
atm_scf = atom_hf.get_atm_nrhf(mol)
mo = []
mo_occ = []
for ia in range(mol.natm):
symb = mol.atom_symbol(ia)
if symb != 'GHOST':
if symb in atm_scf:
e_hf, e, c, occ = atm_scf[symb]
else:
symb = mol.atom_pure_symbol(ia)
e_hf, e, c, occ = atm_scf[symb]
mo.append(c)
mo_occ.append(occ)
mo = scipy.linalg.block_diag(*mo)
mo_occ = numpy.hstack(mo_occ)
pmol = copy.copy(mol)
pmol.cart = False
c = addons.project_mo_nr2nr(pmol, mo, mol)
dm = numpy.dot(c * mo_occ, c.T)
for k, v in atm_scf.items():
logger.debug1(mol, 'Atom %s, E = %.12g', k, v[0])
return dm
def dump_flags(self, verbose=None):
logger.info(self, '\n')
logger.info(self, '******** %s ********', self.__class__)
logger.info(self, 'mesh = %s (%d PWs)', self.mesh, numpy.prod(self.mesh))
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
return self
def get_init_guess(self, mol=None, key='minao'):
if mol is None:
mol = self.mol
if callable(key):
dm = key(mol)
elif key.lower() == '1e':
dm = self.init_guess_by_1e(mol)
elif getattr(mol, 'natm', 0) == 0:
logger.info(self, 'No atom found in mol. Use 1e initial guess')
dm = self.init_guess_by_1e(mol)
elif key.lower() == 'atom':
dm = self.init_guess_by_atom(mol)
elif key.lower().startswith('chk'):
try:
dm = self.init_guess_by_chkfile()
except (IOError, KeyError):
logger.warn(self,
'Fail in reading %s. Use MINAO initial guess',
self.chkfile)
dm = self.init_guess_by_minao(mol)
else:
dm = self.init_guess_by_minao(mol)
if self.verbose >= logger.DEBUG1:
s = self.get_ovlp()
if isinstance(dm, numpy.ndarray) and dm.ndim == 2:
nelec = (dm.T * s).sum()
else: # UHF
nelec = (dm[0].T * s).sum() + (dm[1].T * s).sum()
logger.debug1(self, 'Nelec from initial guess = %g', nelec.real)
return dm
def extrapolate(self, nd=None):
if nd is None:
nd = self.get_num_vec()
if nd == 0:
raise RuntimeError('No vector found in DIIS object.')
h = self._H[:nd + 1, :nd + 1]
g = numpy.zeros(nd + 1, h.dtype)
g[0] = 1
w, v = scipy.linalg.eigh(h)
if numpy.any(abs(w) < 1e-14):
logger.debug(self,
'Linear dependence found in DIIS error vectors.')
idx = abs(w) > 1e-14
c = numpy.dot(v[:, idx] * (1. / w[idx]),
numpy.dot(v[:, idx].T.conj(), g))
else:
try:
c = numpy.linalg.solve(h, g)
except numpy.linalg.linalg.LinAlgError as e:
logger.warn(self, ' diis singular, eigh(h) %s', w)
raise e
logger.debug1(self, 'diis-c %s', c)
xnew = None
for i, ci in enumerate(c[1:]):
xi = self.get_vec(i)
if xnew is None:
xnew = numpy.zeros(xi.size, c.dtype)
for p0, p1 in misc.prange(0, xi.size, BLOCK_SIZE):
xnew[p0:p1] += xi[p0:p1] * ci
return xnew
def dump_flags(self):
log = logger.Logger(self.stdout, self.verbose)
logger.info(self, '\n')
logger.info(self, '******** %s flags ********', self.__class__)
logger.info(self, 'gs = %s', self.gs)
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
def eig(self, f, s):
mol = self.mol
ao_ang = _angular_momentum_for_each_ao(mol)
nao = mol.nao
mo_coeff = []
mo_energy = []
for l in range(param.L_MAX):
degen = 2 * l + 1
idx = numpy.where(ao_ang == l)[0]
nao_l = len(idx)
if nao_l > 0:
nsh = nao_l // degen
f_l = f[idx[:, None], idx].reshape(nsh, degen, nsh, degen)
s_l = s[idx[:, None], idx].reshape(nsh, degen, nsh, degen)
# Average over angular parts
f_l = numpy.einsum('piqi->pq', f_l) / degen
s_l = numpy.einsum('piqi->pq', s_l) / degen
e, c = self._eigh(f_l, s_l)
for i, ei in enumerate(e):
logger.debug1(self, 'l = %d e_%d = %.9g', l, i, ei)
mo_energy.append(numpy.repeat(e, degen))
mo = numpy.zeros((nao, nsh, degen))
for i in range(degen):
mo[idx[i::degen], :, i] = c
mo_coeff.append(mo.reshape(nao, nao_l))
return numpy.hstack(mo_energy), numpy.hstack(mo_coeff)
def nelec(self, mu):
logger.debug1(self.__log__, "| requested mu={:.3e}".format(
mu,
))
result = []
total = 0
for ind, (i, c) in enumerate(zip(self.solvers, self.factors)):
n = i.__hcore__.shape[0] // 2
i.__hcore__[:n, :n] -= numpy.eye(n) * mu
i.kernel()
i.__hcore__[:n, :n] += numpy.eye(n) * mu
dm = i.make_rdm1()
result.append(numpy.diag(dm)[:n].sum())
total += c*result[-1]
logger.debug2(self.__log__, "| solver {:d} E={:.8f}".format(
ind,
i.e_tot,
))
logger.debug1(self.__log__, "| occupations {} = {:.3e}".format(
"+".join("{:.1f}*{:.3f}".format(i, j) for i, j in zip(self.factors, result)),
total,
))
return total-self.target
def make_modchg_basis(auxcell, smooth_eta):
# * chgcell defines smooth gaussian functions for each angular momentum for
# auxcell. The smooth functions may be used to carry the charge
chgcell = copy.copy(auxcell) # smooth model density for coulomb integral to carry charge
half_sph_norm = .5/numpy.sqrt(numpy.pi)
chg_bas = []
chg_env = [smooth_eta]
ptr_eta = auxcell._env.size
ptr = ptr_eta + 1
l_max = auxcell._bas[:,gto.ANG_OF].max()
# gaussian_int(l*2+2) for multipole integral:
# \int (r^l e^{-ar^2} * Y_{lm}) (r^l Y_{lm}) r^2 dr d\Omega
norms = [half_sph_norm/gto.gaussian_int(l*2+2, smooth_eta)
for l in range(l_max+1)]
for ia in range(auxcell.natm):
for l in set(auxcell._bas[auxcell._bas[:,gto.ATOM_OF]==ia, gto.ANG_OF]):
chg_bas.append([ia, l, 1, 1, 0, ptr_eta, ptr, 0])
chg_env.append(norms[l])
ptr += 1
chgcell._atm = auxcell._atm
chgcell._bas = numpy.asarray(chg_bas, dtype=numpy.int32).reshape(-1,gto.BAS_SLOTS)
chgcell._env = numpy.hstack((auxcell._env, chg_env))
chgcell.rcut = _estimate_rcut(smooth_eta, l_max, 1., auxcell.precision)
logger.debug1(auxcell, 'make compensating basis, num shells = %d, num cGTOs = %d',
chgcell.nbas, chgcell.nao_nr())
logger.debug1(auxcell, 'chgcell.rcut %s', chgcell.rcut)
return chgcell
def get_occ(self, mo_energy=None, mo_coeff=None):
'''spherically averaged fractional occupancy'''
mol = self.mol
symb = mol.atom_symbol(0)
nelec_ecp = mol.atom_nelec_core(0)
coreshl = gto.ecp.core_configuration(nelec_ecp)
occ = []
for l in range(param.L_MAX):
n2occ, frac = frac_occ(symb, l, self.atomic_configuration)
degen = 2 * l + 1
idx = mol._bas[:, gto.ANG_OF] == l
nbas_l = mol._bas[idx, gto.NCTR_OF].sum()
if l < 4:
n2occ -= coreshl[l]
assert n2occ <= nbas_l
logger.debug1(self, 'l = %d occ = %d + %.4g', l, n2occ, frac)
occ_l = numpy.zeros(nbas_l)
occ_l[:n2occ] = 2
if frac > 0:
occ_l[n2occ] = frac
occ.append(numpy.repeat(occ_l, degen))
else:
occ.append(numpy.zeros(nbas_l * degen))
return numpy.hstack(occ)
def get_occ(self, mo_energy=None, mo_coeff=None):
if mo_energy is None: mo_energy = self.mo_energy
mol = self.mol
c = lib.param.LIGHT_SPEED
n4c = len(mo_energy)
n2c = n4c // 2
mo_occ = numpy.zeros(n2c * 2)
nocc = mol.nelectron
if mo_energy[n2c] > -1.999 * c**2:
mo_occ[n2c:n2c + nocc] = 1
else:
logger.warn(self, 'Variational collapse. PES mo_energy %g < -2c^2',
mo_energy[n2c])
lumo = mo_energy[mo_energy > -1.999 * c**2][nocc]
mo_occ[mo_energy > -1.999 * c**2] = 1
mo_occ[mo_energy >= lumo] = 0
if self.verbose >= logger.INFO:
if mo_energy[n2c + nocc - 1] + 1e-3 > mo_energy[n2c + nocc]:
logger.warn(self, 'H**O %.15g == LUMO %.15g',
mo_energy[n2c + nocc - 1], mo_energy[n2c + nocc])
else:
logger.info(self, 'H**O %d = %.12g LUMO %d = %.12g', nocc,
mo_energy[n2c + nocc - 1], nocc + 1,
mo_energy[n2c + nocc])
logger.debug1(self, 'NES mo_energy = %s', mo_energy[:n2c])
logger.debug(self, 'PES mo_energy = %s', mo_energy[n2c:])
return mo_occ
def get_occ(mo_energy_kpts=None, mo_coeff=None):
if mo_energy_kpts is None: mo_energy_kpts = mf.mo_energy
mo_energy_kpts = numpy.asarray(mo_energy_kpts)
mo_occ_kpts = numpy.zeros_like(mo_energy_kpts)
logger.debug1(mf, "mo_occ_kpts.shape", mo_occ_kpts.shape)
nkpts = len(mo_energy_kpts[0])
h**o = [-1e8, -1e8]
lumo = [1e8, 1e8]
for k in range(nkpts):
for s in [0, 1]:
e_idx = numpy.argsort(mo_energy_kpts[s, k])
e_sort = mo_energy_kpts[s, k][e_idx]
n = mf.nelec[s]
mo_occ_kpts[s, k, e_idx[:n]] = 1
h**o[s] = max(h**o[s], e_sort[n - 1])
lumo[s] = min(lumo[s], e_sort[n])
for nm, s in zip(['alpha', 'beta'], [0, 1]):
logger.info(mf, nm + ' H**O = %.12g LUMO = %.12g', h**o[s],
lumo[s])
if h**o[s] > lumo[s]:
logger.warn(
mf,
"WARNING! H**O is greater than LUMO! This may result in errors with canonical occupation."
)
return mo_occ_kpts
def get_init_guess(self, mol=None, key='minao'):
if mol is None:
mol = self.mol
if callable(key):
dm = key(mol)
elif key.lower() == '1e':
dm = self.init_guess_by_1e(mol)
elif getattr(mol, 'natm', 0) == 0:
logger.info(self, 'No atom found in mol. Use 1e initial guess')
dm = self.init_guess_by_1e(mol)
elif key.lower() == 'atom':
dm = self.init_guess_by_atom(mol)
elif key.lower() == 'chkfile':
try:
dm = self.init_guess_by_chkfile()
except (IOError, KeyError):
logger.warn(self, 'Fail in reading %s. Use MINAO initial guess',
self.chkfile)
dm = self.init_guess_by_minao(mol)
else:
dm = self.init_guess_by_minao(mol)
if self.verbose >= logger.DEBUG1:
logger.debug1(self, 'Nelec from initial guess = %g',
(dm*self.get_ovlp()).sum().real)
return dm
def get_veff(ks_grad, mol=None, dm=None):
'''Coulomb + XC functional
'''
if mol is None: mol = ks_grad.mol
if dm is None: dm = ks_grad.base.make_rdm1()
t0 = (time.clock(), time.time())
mf = ks_grad.base
ni = mf._numint
if ks_grad.grids is not None:
grids = ks_grad.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
if mf.nlc != '':
raise NotImplementedError
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
mem_now = lib.current_memory()[0]
max_memory = max(2000, ks_grad.max_memory*.9-mem_now)
if ks_grad.grid_response:
exc, vxc = rks_grad.get_vxc_full_response(
ni, mol, grids, mf.xc, dm,
max_memory=max_memory, verbose=ks_grad.verbose)
logger.debug1(ks_grad, 'sum(grids response) %s', exc.sum(axis=0))
else:
exc, vxc = rks_grad.get_vxc(
ni, mol, grids, mf.xc, dm,
max_memory=max_memory, verbose=ks_grad.verbose)
t0 = logger.timer(ks_grad, 'vxc', *t0)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vj = ks_grad.get_j(mol, dm)
vxc += vj
if ks_grad.auxbasis_response:
e1_aux = vj.aux
else:
vj, vk = ks_grad.get_jk(mol, dm)
if ks_grad.auxbasis_response:
vk_aux = vk.aux * hyb
vk *= hyb
if abs(omega) > 1e-10: # For range separated Coulomb operator
raise NotImplementedError
vk_lr = ks_grad.get_k(mol, dm, omega=omega)
vk += vk_lr * (alpha - hyb)
if ks_grad.auxbasis_response:
vk_aux += vk_lr.aux * (alpha - hyb)
vxc += vj - vk * .5
if ks_grad.auxbasis_response:
e1_aux = vj.aux - vk_aux * .5
if ks_grad.auxbasis_response:
logger.debug1(ks_grad, 'sum(auxbasis response) %s', e1_aux.sum(axis=0))
vxc = lib.tag_array(vxc, exc1_grid=exc, aux=e1_aux)
else:
vxc = lib.tag_array(vxc, exc1_grid=exc)
return vxc
def aux_e2(cell, auxcell, intor):
'''3-center AO integrals (ij|L), where L is the auxiliary basis.
Implements double summation over lattice vectors: \sum_{lm} (i[l]j[m]|L[0]).
'''
# sum over largest number of images in either cell or auxcell
nimgs = numpy.max((cell.nimgs, auxcell.nimgs), axis=0)
Ls = tools.pbc.get_lattice_Ls(cell, nimgs)
logger.debug1(cell, "Images summed over in DFT %s", nimgs)
logger.debug2(cell, "Ls = %s", Ls)
nao = cell.nao_nr()
nao_pair = nao*(nao+1) // 2
nao_pair = nao*nao
naoaux = auxcell.nao_nr()
cellL = cell.copy()
cellR = cell.copy()
_envL = cellL._env
_envR = cellR._env
ptr_coord = cellL._atm[:,pyscf.gto.PTR_COORD]
buf = numpy.zeros((nao_pair,naoaux))
for l, L1 in enumerate(Ls):
_envL[ptr_coord+0] = cell._env[ptr_coord+0] + L1[0]
_envL[ptr_coord+1] = cell._env[ptr_coord+1] + L1[1]
_envL[ptr_coord+2] = cell._env[ptr_coord+2] + L1[2]
for m in range(l):
_envR[ptr_coord+0] = cell._env[ptr_coord+0] + Ls[m][0]
_envR[ptr_coord+1] = cell._env[ptr_coord+1] + Ls[m][1]
_envR[ptr_coord+2] = cell._env[ptr_coord+2] + Ls[m][2]
buf += pyscf.df.incore.aux_e2(cellL, auxcell, intor, mol1=cellR)
buf += .5 * pyscf.df.incore.aux_e2(cellL, auxcell, intor, mol1=cellL)
eri = buf.reshape(nao,nao,-1)
return eri + eri.transpose(1,0,2)
def kernel(self,
h1e,
eri,
norb,
nelec,
ci0=None,
ecore=0,
restart=None,
**kwargs):
if restart is None:
restart = self.restart
state_id = min(self.config['eps_vars'])
if restart or ci0 is not None:
if self.verbose >= logger.DEBUG1:
logger.debug1(
self, 'restart was set. wf is read from wf_eps* file.')
self.cleanup(remove_wf=False)
wfn_file = get_wfn_file(self, state_id)
if os.path.isfile(wfn_file):
shutil.move(wfn_file, get_wfn_file(self, state_id * 2))
else:
self.cleanup(remove_wf=True)
if 'orbsym' in kwargs:
self.orbsym = kwargs['orbsym']
writeIntegralFile(self, h1e, eri, norb, nelec, ecore)
conf = {}
if 'tol' in kwargs:
conf['tol'] = kwargs['tol']
write_config(self, nelec, conf)
if self.dryrun:
logger.info(self, 'Only write integrals and config')
if self.nroots == 1:
calc_e = 0.0
roots = ''
else:
calc_e = [0.0] * self.nroots
roots = [''] * self.nroots
return calc_e, roots
if self.nroots != 1:
raise NotImplementedError
execute_shci(self)
if self.verbose >= logger.DEBUG1:
with open(os.path.join(self.runtimedir, self.outputfile),
'r') as f:
self.stdout.write(f.read())
calc_e = read_energy(self)
# Each eps_vars is associated to one approximate wfn.
roots = state_id = min(self.config['eps_vars'])
if not os.path.isfile(get_wfn_file(self, state_id)):
raise RuntimeError('Eigenstate %s not found' %
get_wfn_file(self, state_id))
return calc_e, roots
def get_veff(self, mol=None, dm=None):
vj, vk = self.get_jk(mol, dm)
vhf = vj - vk*.5
if self.auxbasis_response:
e1_aux = vj.aux - vk.aux*.5
logger.debug1(self, 'sum(auxbasis response) %s', e1_aux.sum(axis=0))
vhf = lib.tag_array(vhf, aux=e1_aux)
return vhf
def dump_flags(self):
logger.info(self, '\n')
logger.info(self, '******** %s flags ********', self.__class__)
logger.info(self, 'gs = %s', self.gs)
logger.info(self, 'eta = %s', self.eta)
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
return self
def dump_flags(self):
logger.info(self, '\n')
logger.info(self, '******** %s ********', self.__class__)
logger.info(self, 'mesh = %s (%d PWs)', self.mesh, numpy.prod(self.mesh))
logger.info(self, 'eta = %s', self.eta)
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
return self
def dump_flags(self):
grad_class.dump_flags(self)
logger.info(self, '** Add background charges for %s **', grad_class)
if self.verbose >= logger.DEBUG1:
logger.debug1(self, 'Charge Location')
for i, z in enumerate(charges):
logger.debug1(self, '%.9g %s', z, coords[i])
return self
def get_jk(agf2, eri, rdm1, with_j=True, with_k=True):
''' Get the J/K matrices.
Args:
eri : ndarray or H5 dataset
Electronic repulsion integrals (NOT as _ChemistsERIs)
rdm1 : 2D array
Reduced density matrix
Kwargs:
with_j : bool
Whether to compute J. Default value is True
with_k : bool
Whether to compute K. Default value is True
Returns:
tuple of ndarrays corresponding to J and K, if either are
not requested then they are set to None.
'''
if isinstance(eri, np.ndarray):
vj, vk = _vhf.incore(eri, rdm1, with_j=with_j, with_k=with_k)
else:
nmo = rdm1.shape[0]
npair = nmo * (nmo + 1) // 2
vj = vk = None
if with_j:
rdm1_tril = lib.pack_tril(rdm1 + np.tril(rdm1, k=-1))
vj = np.zeros_like(rdm1_tril)
if with_k:
vk = np.zeros_like(rdm1)
blksize = _agf2.get_blksize(agf2.max_memory, (nmo * npair, nmo**3))
blksize = min(1, max(BLKMIN, blksize))
logger.debug1(agf2, 'blksize (ragf2.get_jk) = %d' % blksize)
tril2sq = lib.square_mat_in_trilu_indices(nmo)
for p0, p1 in lib.prange(0, nmo, blksize):
idx = list(np.concatenate(tril2sq[p0:p1]))
eri0 = eri[idx]
# vj built in tril layout with scaled rdm1_tril
if with_j:
vj[idx] = np.dot(eri0, rdm1_tril)
if with_k:
eri0 = lib.unpack_tril(eri0, axis=-1)
eri0 = eri0.reshape(p1 - p0, nmo, nmo, nmo)
vk[p0:p1] = lib.einsum('ijkl,jk->il', eri0, rdm1)
if with_j:
vj = lib.unpack_tril(vj)
return vj, vk
def get_veff(ks_grad, mol=None, dm=None):
'''
First order derivative of DFT effective potential matrix (wrt electron coordinates)
Args:
ks_grad : grad.uhf.Gradients or grad.uks.Gradients object
'''
if mol is None: mol = ks_grad.mol
if dm is None: dm = ks_grad.base.make_rdm1()
t0 = (time.clock(), time.time())
mf = ks_grad.base
ni = mf._numint
if ks_grad.grids is not None:
grids = ks_grad.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
if mf.nlc != '':
raise NotImplementedError
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
mem_now = lib.current_memory()[0]
max_memory = max(2000, ks_grad.max_memory * .9 - mem_now)
if ks_grad.grid_response:
exc, vxc = get_vxc_full_response(ni,
mol,
grids,
mf.xc,
dm,
max_memory=max_memory,
verbose=ks_grad.verbose)
logger.debug1(ks_grad, 'sum(grids response) %s', exc.sum(axis=0))
else:
exc, vxc = get_vxc(ni,
mol,
grids,
mf.xc,
dm,
max_memory=max_memory,
verbose=ks_grad.verbose)
t0 = logger.timer(ks_grad, 'vxc', *t0)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vj = ks_grad.get_j(mol, dm)
vxc += vj
else:
vj, vk = ks_grad.get_jk(mol, dm)
vk *= hyb
if abs(omega) > 1e-10: # For range separated Coulomb operator
with mol.with_range_coulomb(omega):
vk += ks_grad.get_k(mol, dm) * (alpha - hyb)
vxc += vj - vk * .5
return lib.tag_array(vxc, exc1_grid=exc)
def make_rdm1(self, mo_coeff=None, mo_occ=None):
if mo_coeff is None:
mo_coeff = self.mo_coeff_on_imp
if mo_occ is None:
mo_occ = self.mo_occ
nbf = mo_coeff.shape[0]
mo = mo_coeff[:,mo_occ>0]
dm = numpy.dot(mo, mo.T.conj()) * 2
log.debug1(self, 'density.diag = %s', dm.diagonal())
return dm
def dump_flags(self, verbose=None):
grad_class.dump_flags(self, verbose)
logger.info(self, '** Add background charges for %s **', grad_class)
if self.verbose >= logger.DEBUG1:
logger.debug1(self, 'Charge Location')
coords = self.base.mm_mol.atom_coords()
charges = self.base.mm_mol.atom_charges()
for i, z in enumerate(charges):
logger.debug1(self, '%.9g %s', z, coords[i])
return self
def molecular_response_ov(vind, space_ov, nocc, nmo, double, log_dest):
"""
Retrieves a raw response matrix.
Args:
vind (Callable): a pyscf matvec routine;
space_ov (ndarray): the active `ov` space mask: either the same mask for both rows and columns (1D array) or
separate `ov` masks for rows and columns (2D array);
nocc (int): the number of occupied orbitals (frozen and active);
nmo (int): the total number of orbitals;
double (bool): set to True if `vind` returns the double-sized (i.e. full) matrix;
log_dest (object): pyscf logging;
Returns:
The TD matrix.
Note:
The runtime scales with the size of the column mask `space_ov[1]` but not the row mask `space_ov[0]`.
"""
space_ov = numpy.array(space_ov)
nocc_full, nvirt_full = nocc, nmo - nocc
size_full = nocc_full * nvirt_full
if space_ov.shape not in ((size_full, ), (2, size_full)):
raise ValueError(
"The 'space_ov' argument should be a 1D array with dimension {size_full:d} or a 2D array with"
" dimensions 2x{size_full:d}, found: {actual}".format(
size_full=size_full,
actual=space_ov.shape,
))
ov1, ov2 = space_ov
size = sum(ov2)
probe = numpy.zeros((size, 2 * size_full if double else size_full))
probe[numpy.arange(probe.shape[0]), numpy.argwhere(ov2)[:, 0]] = 1
logger.debug1(
log_dest,
"Requesting response against {} matrix (column space: {})".format(
"x".join(str(i) for i in probe.shape),
format_mask(ov2),
))
result = vind(probe).T
logger.debug1(log_dest, " output: {}".format(result.shape))
if double:
result = result[numpy.tile(ov1, 2)]
half = sum(ov1)
result_a = result[:half]
result_b = result[half:]
return result_a, -result_b.conj()
else:
return result[ov1]
def update(self, x, xerr=None):
'''Extrapolate vector
* If xerr the error vector is given, this function will push the target
vector and error vector in the DIIS subspace, and use the error vector
to extrapolate the vector and return the extrapolated vector.
* If xerr is None, this function will take the difference between
the current given vector and the last given vector as the error
vector to extrapolate the vector.
'''
if xerr is not None:
self.push_err_vec(xerr)
self.push_vec(x)
nd = self.get_num_vec()
if nd < self.min_space:
return x
dt = numpy.array(self.get_err_vec(self._head - 1), copy=False)
for i in range(nd):
tmp = 0
dti = self.get_err_vec(i)
for p0, p1 in prange(0, dt.size, BLOCK_SIZE):
tmp += numpy.dot(dt[p0:p1].conj(), dti[p0:p1])
self._H[self._head, i + 1] = tmp
self._H[i + 1, self._head] = tmp.conjugate()
dt = None
h = self._H[:nd + 1, :nd + 1]
g = numpy.zeros(nd + 1, x.dtype)
g[0] = 1
#try:
# c = numpy.linalg.solve(h, g)
#except numpy.linalg.linalg.LinAlgError:
# logger.warn(self, ' diis singular')
if 1:
w, v = scipy.linalg.eigh(h)
idx = abs(w) > 1e-14
c = numpy.dot(v[:, idx] * (1 / w[idx]),
numpy.dot(v[:, idx].T.conj(), g))
logger.debug1(self, 'diis-c %s', c)
if self._xprev is None:
xnew = numpy.zeros_like(x.ravel())
else:
self._xprev = None # release memory first
self._xprev = xnew = numpy.zeros_like(x.ravel())
for i, ci in enumerate(c[1:]):
xi = self.get_vec(i)
for p0, p1 in prange(0, x.size, BLOCK_SIZE):
xnew[p0:p1] += xi[p0:p1] * ci
return xnew.reshape(x.shape)
def update(self, x, xerr=None):
'''Extrapolate vector
* If xerr the error vector is given, this function will push the target
vector and error vector in the DIIS subspace, and use the error vector
to extrapolate the vector and return the extrapolated vector.
* If xerr is None, this function will take the difference between
the current given vector and the last given vector as the error
vector to extrapolate the vector.
'''
if xerr is not None:
self.push_err_vec(xerr)
self.push_vec(x)
nd = self.get_num_vec()
if nd < self.min_space:
return x
dt = numpy.array(self.get_err_vec(self._head-1), copy=False)
for i in range(nd):
tmp = 0
dti = self.get_err_vec(i)
for p0,p1 in prange(0, dt.size, BLOCK_SIZE):
tmp += numpy.dot(dt[p0:p1].conj(), dti[p0:p1])
self._H[self._head,i+1] = tmp
self._H[i+1,self._head] = tmp.conjugate()
dt = None
h = self._H[:nd+1,:nd+1]
g = numpy.zeros(nd+1, x.dtype)
g[0] = 1
#try:
# c = numpy.linalg.solve(h, g)
#except numpy.linalg.linalg.LinAlgError:
# logger.warn(self, ' diis singular')
if 1:
w, v = scipy.linalg.eigh(h)
idx = abs(w)>1e-14
c = numpy.dot(v[:,idx]*(1/w[idx]), numpy.dot(v[:,idx].T.conj(), g))
logger.debug1(self, 'diis-c %s', c)
if self._xprev is None:
xnew = numpy.zeros_like(x.ravel())
else:
self._xprev = None # release memory first
self._xprev = xnew = numpy.zeros_like(x.ravel())
for i, ci in enumerate(c[1:]):
xi = self.get_vec(i)
for p0,p1 in prange(0, x.size, BLOCK_SIZE):
xnew[p0:p1] += xi[p0:p1] * ci
return xnew.reshape(x.shape)
def dump_flags(self):
log = logger.Logger(self.stdout, self.verbose)
logger.info(self, '\n')
logger.info(self, '******** %s flags ********', self.__class__)
logger.info(self, 'gs = %s', self.gs)
logger.info(self, 'auxbasis = %s', self.auxbasis)
logger.info(self, 'eta = %s', self.eta)
if isinstance(self._cderi, str):
logger.info(self, '_cderi = %s', self._cderi)
else:
logger.info(self, '_cderi = %s', self._cderi_file.name)
logger.info(self, 'len(kpts) = %d', len(self.kpts))
logger.debug1(self, ' kpts = %s', self.kpts)
def DMRG_MPS_NEVPT(mc, root=0, fcisolver=None,maxm = 500, tol =1e-6, parallel= True):
if (isinstance(mc, basestring)):
fh5 = h5py.File(mc,'r')
mol = eval(fh5['mol'].value)
ncas = fh5['mc/ncas'].value
ncore = fh5['mc/ncore'].value
nvirt = fh5['mc/nvirt'].value
nelecas = fh5['mc/nelecas'].value
fh5.close()
mc_chk = mc
else :
mol = mc.mol
ncas = mc.ncas
ncore = mc.ncore
nvirt = mc.mo_coeff.shape[1] - mc.ncas-mc.ncore
nelecas = mc.nelecas
mc_chk = 'mc_chkfile'
write_chk(mc,root,mc_chk)
if fcisolver is None:
fcisolver = DMRGCI(mol, maxm, tol)
fcisolver.twopdm = False
fcisolver.nroots = mc.fcisolver.nroots
scratch = fcisolver.scratchDirectory
fcisolver.scratchDirectory = ''
#if (not parallel):
# ci.extraline.append('restart_mps_nevpt %d %d %d'%(ncas,ncore, nvirt))
fcisolver.extraline.append('fullrestart')
fcisolver.extraline.append('nevpt_state_num %d'%root)
writeDMRGConfFile(nelecas[0], nelecas[1], False, fcisolver)
fcisolver.scratchDirectory = scratch
if fcisolver.verbose >= logger.DEBUG1:
inFile = fcisolver.configFile
#inFile = os.path.join(self.scratchDirectory,self.configFile)
logger.debug1(fcisolver, 'Block Input conf')
logger.debug1(fcisolver, open(inFile, 'r').read())
from subprocess import check_call
import os
full_path = os.path.realpath(__file__)
check_call('%s %s/nevpt_mpi.py %s %s %s %s %s'%(fcisolver.mpiprefix, os.path.dirname(full_path), mc_chk, fcisolver.executable, fcisolver.configFile,fcisolver.outputFile, fcisolver.scratchDirectory), shell=True)
def update(self, s, d, f):
if isinstance(f, numpy.ndarray) and f.ndim == 2:
sdf = reduce(numpy.dot, (s,d,f))
errvec = sdf.T.conj() - sdf
else:
sdf_a = reduce(numpy.dot, (s, d[0], f[0]))
sdf_b = reduce(numpy.dot, (s, d[1], f[1]))
errvec = numpy.hstack((sdf_a.T.conj() - sdf_a,
sdf_b.T.conj() - sdf_b))
logger.debug1(self, 'diis-norm(errvec)=%g', numpy.linalg.norm(errvec))
xnew = pyscf.lib.diis.DIIS.update(self, f, xerr=errvec)
if self.rollback > 0 and len(self._bookkeep) == self.space:
self._bookkeep = self._bookkeep[-self.rollback:]
return xnew
def kernel(self, h1e, eri, norb, nelec, ci0=None, ecore=0, restart=None,
**kwargs):
if restart is None:
restart = self.restart
state_id = min(self.config['eps_vars'])
if restart or ci0 is not None:
if self.verbose >= logger.DEBUG1:
logger.debug1(self, 'restart was set. wf is read from wf_eps* file.')
self.cleanup(remove_wf=False)
wfn_file = get_wfn_file(self, state_id)
if os.path.isfile(wfn_file):
shutil.move(wfn_file, get_wfn_file(self, state_id * 2))
else:
self.cleanup(remove_wf=True)
if 'orbsym' in kwargs:
self.orbsym = kwargs['orbsym']
writeIntegralFile(self, h1e, eri, norb, nelec, ecore)
conf = {}
if 'tol' in kwargs:
conf['tol'] = kwargs['tol']
write_config(self, nelec, conf)
if self.dryrun:
logger.info(self, 'Only write integrals and config')
if self.nroots == 1:
calc_e = 0.0
roots = ''
else :
calc_e = [0.0] * self.nroots
roots = [''] * self.nroots
return calc_e, roots
if self.nroots != 1:
raise NotImplementedError
execute_shci(self)
if self.verbose >= logger.DEBUG1:
with open(os.path.join(self.runtimedir, self.outputfile), 'r') as f:
self.stdout.write(f.read())
calc_e = read_energy(self)
# Each eps_vars is associated to one approximate wfn.
roots = state_id = min(self.config['eps_vars'])
if not os.path.isfile(get_wfn_file(self, state_id)):
raise RuntimeError('Eigenstate %s not found' % get_wfn_file(self, state_id))
return calc_e, roots
def dump_flags(self):
log = logger.Logger(self.stdout, self.verbose)
logger.info(self, "\n")
logger.info(self, "******** %s flags ********", self.__class__)
logger.info(self, "gs = %s", self.gs)
logger.info(self, "metric = %s", self.metric)
logger.info(self, "approx_sr_level = %s", self.approx_sr_level)
logger.info(self, "auxbasis = %s", self.auxbasis)
logger.info(self, "eta = %s", self.eta)
if isinstance(self._cderi, str):
logger.info(self, "_cderi = %s", self._cderi)
else:
logger.info(self, "_cderi = %s", self._cderi_file.name)
logger.info(self, "len(kpts) = %d", len(self.kpts))
logger.debug1(self, " kpts = %s", self.kpts)
def get_veff(ks_grad, mol=None, dm=None):
'''Coulomb + XC functional
'''
if mol is None: mol = ks_grad.mol
if dm is None: dm = ks_grad.base.make_rdm1()
t0 = (time.clock(), time.time())
mf = ks_grad.base
ni = mf._numint
if ks_grad.grids is not None:
grids = ks_grad.grids
else:
grids = mf.grids
if grids.coords is None:
grids.build(with_non0tab=True)
if mf.nlc != '':
raise NotImplementedError
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
mem_now = lib.current_memory()[0]
max_memory = max(2000, ks_grad.max_memory*.9-mem_now)
if ks_grad.grid_response:
exc, vxc = get_vxc_full_response(ni, mol, grids, mf.xc, dm,
max_memory=max_memory,
verbose=ks_grad.verbose)
logger.debug1(ks_grad, 'sum(grids response) %s', exc.sum(axis=0))
else:
exc, vxc = get_vxc(ni, mol, grids, mf.xc, dm,
max_memory=max_memory, verbose=ks_grad.verbose)
t0 = logger.timer(ks_grad, 'vxc', *t0)
if abs(hyb) < 1e-10:
vj = ks_grad.get_j(mol, dm)
vxc += vj[0] + vj[1]
else:
vj, vk = ks_grad.get_jk(mol, dm)
vk *= hyb
if abs(omega) > 1e-10: # For range separated Coulomb operator
with mol.with_range_coulomb(omega):
vk += ks_grad.get_k(mol, dm) * (alpha - hyb)
vxc += vj[0] + vj[1] - vk
return lib.tag_array(vxc, exc1_grid=exc)
def eig(self, f, s):
atm = self.mol
symb = atm.atom_symbol(0)
idx_by_l = [[] for i in range(param.L_MAX)]
i0 = 0
for ib in range(atm.nbas):
l = atm.bas_angular(ib)
nc = atm.bas_nctr(ib)
i1 = i0 + nc * (l*2+1)
idx_by_l[l].extend(range(i0, i1, l*2+1))
i0 = i1
nbf = atm.nao_nr()
self._occ = numpy.zeros(nbf)
mo_c = numpy.zeros((nbf, nbf))
mo_e = numpy.zeros(nbf)
# fraction occupation
for l in range(param.L_MAX):
if idx_by_l[l]:
n2occ, frac = frac_occ(symb, l)
logger.debug1(self, 'l = %d occ = %d + %.4g', l, n2occ, frac)
idx = numpy.array(idx_by_l[l])
f1 = 0
s1 = 0
for m in range(l*2+1):
f1 = f1 + f[idx+m,:][:,idx+m]
s1 = s1 + s[idx+m,:][:,idx+m]
f1 *= 1./(l*2+1)
s1 *= 1./(l*2+1)
e, c = hf.SCF.eig(self, f1, s1)
for i, ei in enumerate(e):
logger.debug1(self, 'l = %d e_%d = %.9g', l, i, ei)
for m in range(l*2+1):
mo_e[idx] = e
self._occ[idx[:n2occ]] = 2
if frac > 1e-15:
self._occ[idx[n2occ]] = frac
for i,i1 in enumerate(idx):
mo_c[idx,i1] = c[:,i]
idx += 1
return mo_e, mo_c
def update(self, s, d, f, mf, h1e, vhf):
if self._head >= self.space:
self._head = 0
if not self._buffer:
shape = (self.space,) + f.shape
self._buffer['dm' ] = numpy.zeros(shape, dtype=f.dtype)
self._buffer['fock'] = numpy.zeros(shape, dtype=f.dtype)
self._buffer['dm' ][self._head] = d
self._buffer['fock'][self._head] = f
ds = self._buffer['dm' ]
fs = self._buffer['fock']
fun, c = adiis_minimize(ds, fs, self._head)
if self.verbose >= logger.DEBUG1:
etot = mf.energy_elec(d, h1e, vhf)[0] + fun
logger.debug1(self, 'E %s diis-c %s ', etot, c)
fock = numpy.einsum('i,i...pq->...pq', c, fs)
self._head += 1
return fock
def hot_load(envs):
try:
with open(configfile) as f:
# filter out comments
raw_js = []
balance = 0
data = [x for x in f.readlines()
if not x.startswith('#') and x.rstrip()]
for n, line in enumerate(data):
if not line.lstrip().startswith('#'):
raw_js.append(line)
balance += line.count('{') - line.count('}')
if balance == 0:
break
raw_py = ''.join(data[n+1:])
raw_js = ''.join(raw_js)
logger.debug(casscf, 'Reading CASSCF parameters from config file %s',
os.path.realpath(configfile))
logger.debug1(casscf, ' Inject casscf settings %s', raw_js)
conf = json.loads(raw_js)
casscf.__dict__.update(conf.pop('casscf'))
# Not yet found a way to update locals() on the runtime
# https://docs.python.org/2/library/functions.html#locals
#for k in conf:
# if k in envs:
# logger.info(casscf, 'Update envs[%s] = %s', k, conf[k])
# envs[k] = conf[k]
logger.debug1(casscf, ' Inject python script\n%s\n', raw_py)
if len(raw_py.strip()) > 0:
if sys.version_info >= (3,):
# A hacky call using eval because exec are so different in python2 and python3
eval(compile('exec(raw_py, envs, {})', '<str>', 'exec'))
else:
eval(compile('exec raw_py in envs, {}', '<str>', 'exec'))
except Exception as e:
logger.warn(casscf, 'CASSCF hot_load error %s', e)
logger.warn(casscf, ''.join(traceback.format_exc()))
if callable(old_cb):
old_cb(envs)
def update(self, x, xerr=None):
'''use DIIS method to solve Eq. operator(x) = x.'''
if xerr is not None:
self.push_err_vec(xerr)
self.push_vec(x)
nd = self.get_num_vec()
if nd < self.min_space:
return x
dt = numpy.array(self.get_err_vec(self._head-1), copy=False)
for i in range(nd):
tmp = 0
dti = self.get_err_vec(i)
for p0,p1 in prange(0, x.size, BLOCK_SIZE):
tmp += numpy.dot(dt[p0:p1].conj(), dti[p0:p1])
self._H[self._head,i+1] = tmp
self._H[i+1,self._head] = tmp.conjugate()
dt = None
h = self._H[:nd+1,:nd+1]
g = numpy.zeros(nd+1, x.dtype)
g[0] = 1
try:
c = numpy.linalg.solve(h, g)
except numpy.linalg.linalg.LinAlgError:
logger.warn(self, 'singularity in diis')
w, v = scipy.linalg.eigh(h)
idx = abs(w)>1e-14
c = numpy.dot(v[:,idx]*(1/w[idx]), numpy.dot(v[:,idx].T.conj(), g))
logger.debug1(self, 'diis-c %s', c)
if self._xprev is None:
xnew = numpy.zeros_like(x.ravel())
else:
self._xprev = None # release memory first
self._xprev = xnew = numpy.zeros_like(x.ravel())
for i, ci in enumerate(c[1:]):
xi = self.get_vec(i)
for p0,p1 in prange(0, x.size, BLOCK_SIZE):
xnew[p0:p1] += xi[p0:p1] * ci
return xnew.reshape(x.shape)
def update(self, s, d, f, mf, h1e, vhf):
if self._head >= self.space:
self._head = 0
if not self._buffer:
shape = (self.space,) + f.shape
self._buffer['dm' ] = numpy.zeros(shape, dtype=f.dtype)
self._buffer['fock'] = numpy.zeros(shape, dtype=f.dtype)
self._buffer['etot'] = numpy.zeros(self.space)
self._buffer['dm' ][self._head] = d
self._buffer['fock'][self._head] = f
self._buffer['etot'][self._head] = mf.energy_elec(d, h1e, vhf)[0]
self._head += 1
ds = self._buffer['dm' ]
fs = self._buffer['fock']
es = self._buffer['etot']
etot, c = ediis_minimize(es, ds, fs)
logger.debug1(self, 'E %s diis-c %s', etot, c)
fock = numpy.einsum('i,i...pq->...pq', c, fs)
return fock
def get_occ(self, mo_energy=None, mo_coeff=None):
if mo_energy is None: mo_energy = self.mo_energy
mol = self.mol
c = lib.param.LIGHT_SPEED
n4c = len(mo_energy)
n2c = n4c // 2
mo_occ = numpy.zeros(n2c * 2)
if mo_energy[n2c] > -1.999 * c**2:
mo_occ[n2c:n2c+mol.nelectron] = 1
else:
lumo = mo_energy[mo_energy > -1.999 * c**2][mol.nelectron]
mo_occ[mo_energy > -1.999 * c**2] = 1
mo_occ[mo_energy >= lumo] = 0
if self.verbose >= logger.INFO:
logger.info(self, 'H**O %d = %.12g LUMO %d = %.12g',
n2c+mol.nelectron, mo_energy[n2c+mol.nelectron-1],
n2c+mol.nelectron+1, mo_energy[n2c+mol.nelectron])
logger.debug1(self, 'NES mo_energy = %s', mo_energy[:n2c])
logger.debug(self, 'PES mo_energy = %s', mo_energy[n2c:])
return mo_occ
def extract_frag_energy(self, emb, dm1, e2frag):
nimp = len(emb.bas_on_frag)
hcore = emb._pure_hcore
hfdm = self.entire_scf.make_rdm1(self.entire_scf.mo_coeff,
self.entire_scf.mo_occ)
vhf = emb.mat_ao2impbas(emb.entire_scf.get_veff(self.mol, hfdm))
e = numpy.dot(dm1[:nimp].flatten(), hcore[:nimp].flatten()) \
+ numpy.dot(dm1[:nimp].flatten(), vhf[:nimp].flatten()) * .5
#emb.mo_coeff_on_imp are changed in function embscf_
#hfdm = emb.make_rdm1(emb.mo_coeff_on_imp, emb.mo_occ)
# Note the cancellation of h1e[part] dot dm1[part] in HF and FCI energy expression
c = scipy.linalg.eigh(emb._project_fock)[1]
hfdm = emb.make_rdm1(c, emb.mo_occ)
vhfemb = emb.get_veff(emb.mol, hfdm)
ecorr = e2frag - numpy.dot(dm1[:nimp].flatten(),
vhfemb[:nimp].flatten())*.5
log.debug1(self, ' FCI pTraceSys = %.12g, ecorr = %12g', e2frag, ecorr)
e_frag = e + ecorr
nelec_frag = dm1[:nimp].trace()
return e_frag, nelec_frag
def kernel(self, h1e, eri, norb, nelec, fciRestart=None, **kwargs):
if self.nroots == 1:
roots = 0
else:
roots = range(self.nroots)
if fciRestart is None:
fciRestart = self.restart or self._restart
writeIntegralFile(self, h1e, eri, norb, nelec)
writeDMRGConfFile(self, nelec, fciRestart)
if self.verbose >= logger.DEBUG1:
inFile = os.path.join(self.runtimeDir, self.configFile)
logger.debug1(self, 'Block Input conf')
logger.debug1(self, open(inFile, 'r').read())
if self.onlywriteIntegral:
logger.info(self, 'Only write integral')
try:
calc_e = readEnergy(self)
except IOError:
if self.nroots == 1:
calc_e = 0.0
else :
calc_e = [0.0] * self.nroots
return calc_e, roots
executeBLOCK(self)
if self.verbose >= logger.DEBUG1:
outFile = os.path.join(self.runtimeDir, self.outputFile)
logger.debug1(self, open(outFile).read())
calc_e = readEnergy(self)
return calc_e, roots
def energy_elec(mf, dm, h1e=None, vhf=None):
r'''Electronic part of Hartree-Fock energy, for given core hamiltonian and
HF potential
... math::
E = \sum_{ij}h_{ij} \gamma_{ji}
+ \frac{1}{2}\sum_{ijkl} \gamma_{ji}\gamma_{lk} \langle ik||jl\rangle
Args:
mf : an instance of SCF class
dm : 2D ndarray
one-partical density matrix
Kwargs:
h1e : 2D ndarray
Core hamiltonian
vhf : 2D ndarray
HF potential
Returns:
Hartree-Fock electronic energy and the 2-electron part contribution
Examples:
>>> from pyscf import gto, scf
>>> mol = gto.M(atom='H 0 0 0; H 0 0 1.1')
>>> mf = scf.RHF(mol)
>>> mf.scf()
>>> dm = mf.make_rdm1()
>>> scf.hf.energy_elec(mf, dm)
(-1.5176090667746334, 0.60917167853723675)
'''
if h1e is None: h1e = mf.get_hcore()
if vhf is None: vhf = mf.get_veff(mf.mol, dm)
e1 = numpy.einsum('ji,ji', h1e.conj(), dm).real
e_coul = numpy.einsum('ji,ji', vhf.conj(), dm).real * .5
logger.debug1(mf, 'E_coul = %.15g', e_coul)
return e1+e_coul, e_coul
def approx_kernel(self, h1e, eri, norb, nelec, fciRestart=None, **kwargs):
fciRestart = True
if isinstance(nelec, (int, numpy.integer)):
neleca = nelec//2 + nelec%2
nelecb = nelec - neleca
else :
neleca, nelecb = nelec
writeIntegralFile(h1e, eri, norb, neleca, nelecb, self)
writeDMRGConfFile(neleca, nelecb, fciRestart, self, True)
if self.verbose >= logger.DEBUG1:
inFile = self.configFile
#inFile = os.path.join(self.scratchDirectory,self.configFile)
logger.debug1(self, 'Block Input conf')
logger.debug1(self, open(inFile, 'r').read())
executeBLOCK(self)
if self.verbose >= logger.DEBUG1:
outFile = self.outputFile
#outFile = os.path.join(self.scratchDirectory,self.outputFile)
logger.debug1(self, open(outFile).read())
calc_e = readEnergy(self)
if self.nroots==1:
roots = 0
else:
roots = range(self.nroots)
return calc_e, roots
def nevpt_intermediate(self, tag, norb, nelec, state, **kwargs):
if self.has_nevpt == False:
writeDMRGConfFile(self, nelec, True,
with_2pdm=False, extraline=['restart_nevpt2_npdm'])
if self.verbose >= logger.DEBUG1:
inFile = os.path.join(self.runtimeDir, self.configFile)
logger.debug1(self, 'Block Input conf')
logger.debug1(self, open(inFile, 'r').read())
executeBLOCK(self)
if self.verbose >= logger.DEBUG1:
outFile = os.path.join(self.runtimeDir, self.outputFile)
logger.debug1(self, open(outFile).read())
self.has_nevpt = True
a16 = numpy.zeros( (norb, norb, norb, norb, norb, norb) )
filename = "%s_matrix.%d.%d.txt" % (tag, state, state)
with open(os.path.join(self.scratchDirectory, "node0", filename), "r") as f:
norb_read = int(f.readline().split()[0])
assert(norb_read == norb)
for line in f:
linesp = line.split()
i, j, k, l, m, n = [int(x) for x in linesp[:6]]
a16[i,j,k,l,m,n] = float(linesp[6])
return a16
