def ccsd(self, t1=None, t2=None, eris=None, **kwargs):
if eris is None: eris = self.ao2mo(self.mo_coeff)
e_corr, self.t1, self.t2 = ccsd.CCSD.ccsd(self, t1, t2, eris)
if getattr(eris, 'orbspin', None) is not None:
self.t1 = lib.tag_array(self.t1, orbspin=eris.orbspin)
self.t2 = lib.tag_array(self.t2, orbspin=eris.orbspin)
return e_corr, self.t1, self.t2
def _common_init_(self, mycc, mo_coeff=None):
if mo_coeff is None:
mo_coeff = mycc.mo_coeff
mo_idx = ccsd.get_frozen_mask(mycc)
if getattr(mo_coeff, 'orbspin', None) is not None:
self.orbspin = mo_coeff.orbspin[mo_idx]
mo_coeff = lib.tag_array(mo_coeff[:,mo_idx], orbspin=self.orbspin)
self.mo_coeff = mo_coeff
else:
orbspin = scf.ghf.guess_orbspin(mo_coeff)
self.mo_coeff = mo_coeff = mo_coeff[:,mo_idx]
if not np.any(orbspin == -1):
self.orbspin = orbspin[mo_idx]
self.mo_coeff = lib.tag_array(mo_coeff, orbspin=self.orbspin)
# Note: Recomputed fock matrix since SCF may not be fully converged.
dm = mycc._scf.make_rdm1(mycc.mo_coeff, mycc.mo_occ)
fockao = mycc._scf.get_fock(dm=dm)
self.fock = reduce(np.dot, (mo_coeff.conj().T, fockao, mo_coeff))
self.nocc = mycc.nocc
mo_e = self.mo_energy = self.fock.diagonal().real
gap = abs(mo_e[:self.nocc,None] - mo_e[None,self.nocc:]).min()
if gap < 1e-5:
logger.warn(mycc, 'H**O-LUMO gap %s too small for GCCSD', gap)
return self
def ccsd(self, t1=None, t2=None, eris=None, mbpt2=False):
'''Ground-state unrestricted (U)CCSD.
Kwargs:
mbpt2 : bool
Use one-shot MBPT2 approximation to CCSD.
'''
if mbpt2:
from pyscf.mp import gmp2
pt = gmp2.GMP2(self._scf, self.frozen, self.mo_coeff, self.mo_occ)
self.e_corr, self.t2 = pt.kernel(eris=eris)
nocc, nvir = self.t2.shape[1:3]
self.t1 = np.zeros((nocc,nvir))
return self.e_corr, self.t1, self.t2
# Initialize orbspin so that we can attach it to t1, t2
if getattr(self.mo_coeff, 'orbspin', None) is None:
orbspin = scf.ghf.guess_orbspin(self.mo_coeff)
if not np.any(orbspin == -1):
self.mo_coeff = lib.tag_array(self.mo_coeff, orbspin=orbspin)
if eris is None: eris = self.ao2mo(self.mo_coeff)
e_corr, self.t1, self.t2 = ccsd.CCSD.ccsd(self, t1, t2, eris)
if getattr(eris, 'orbspin', None) is not None:
self.t1 = lib.tag_array(self.t1, orbspin=eris.orbspin)
self.t2 = lib.tag_array(self.t2, orbspin=eris.orbspin)
return e_corr, self.t1, self.t2
def _finalize(self):
uhf.UHF._finalize(self)
ea = numpy.hstack(self.mo_energy[0])
eb = numpy.hstack(self.mo_energy[1])
# Using mergesort because it is stable. We don't want to change the
# ordering of the symmetry labels when two orbitals are degenerated.
oa_sort = numpy.argsort(ea[self.mo_occ[0]>0 ].round(9), kind='mergesort')
va_sort = numpy.argsort(ea[self.mo_occ[0]==0].round(9), kind='mergesort')
ob_sort = numpy.argsort(eb[self.mo_occ[1]>0 ].round(9), kind='mergesort')
vb_sort = numpy.argsort(eb[self.mo_occ[1]==0].round(9), kind='mergesort')
idxa = numpy.arange(ea.size)
idxa = numpy.hstack((idxa[self.mo_occ[0]> 0][oa_sort],
idxa[self.mo_occ[0]==0][va_sort]))
idxb = numpy.arange(eb.size)
idxb = numpy.hstack((idxb[self.mo_occ[1]> 0][ob_sort],
idxb[self.mo_occ[1]==0][vb_sort]))
self.mo_energy = (ea[idxa], eb[idxb])
orbsyma, orbsymb = get_orbsym(self.mol, self.mo_coeff)
self.mo_coeff = (lib.tag_array(self.mo_coeff[0][:,idxa], orbsym=orbsyma[idxa]),
lib.tag_array(self.mo_coeff[1][:,idxb], orbsym=orbsymb[idxb]))
self.mo_occ = (self.mo_occ[0][idxa], self.mo_occ[1][idxb])
if self.chkfile:
chkfile.dump_scf(self.mol, self.chkfile, self.e_tot, self.mo_energy,
self.mo_coeff, self.mo_occ, overwrite_mol=False)
return self
def eig(self, h, s):
mol = self.mol
if not mol.symmetry:
return self._eigh(h, s)
nirrep = mol.symm_orb.__len__()
s = symm.symmetrize_matrix(s, mol.symm_orb)
ha = symm.symmetrize_matrix(h[0], mol.symm_orb)
cs = []
es = []
orbsym = []
for ir in range(nirrep):
e, c = self._eigh(ha[ir], s[ir])
cs.append(c)
es.append(e)
orbsym.append([mol.irrep_id[ir]] * e.size)
ea = numpy.hstack(es)
ca = hf_symm.so2ao_mo_coeff(mol.symm_orb, cs)
ca = lib.tag_array(ca, orbsym=numpy.hstack(orbsym))
hb = symm.symmetrize_matrix(h[1], mol.symm_orb)
cs = []
es = []
orbsym = []
for ir in range(nirrep):
e, c = self._eigh(hb[ir], s[ir])
cs.append(c)
es.append(e)
orbsym.append([mol.irrep_id[ir]] * e.size)
eb = numpy.hstack(es)
cb = hf_symm.so2ao_mo_coeff(mol.symm_orb, cs)
cb = lib.tag_array(cb, orbsym=numpy.hstack(orbsym))
return (ea,eb), (ca,cb)
def spatial2spin(tx, orbspin=None):
'''Convert T1/T2 of spatial orbital representation to T1/T2 of
spin-orbital representation
'''
if isinstance(tx, numpy.ndarray) and tx.ndim == 2:
# RCCSD t1 amplitudes
return spatial2spin((tx,tx), orbspin)
elif isinstance(tx, numpy.ndarray) and tx.ndim == 4:
# RCCSD t2 amplitudes
t2aa = tx - tx.transpose(0,1,3,2)
return spatial2spin((t2aa,tx,t2aa), orbspin)
elif len(tx) == 2: # t1
t1a, t1b = tx
nocc_a, nvir_a = t1a.shape
nocc_b, nvir_b = t1b.shape
else:
t2aa, t2ab, t2bb = tx
nocc_a, nocc_b, nvir_a, nvir_b = t2ab.shape
if orbspin is None:
orbspin = numpy.zeros((nocc_a+nvir_a)*2, dtype=int)
orbspin[1::2] = 1
nocc = nocc_a + nocc_b
nvir = nvir_a + nvir_b
idxoa = numpy.where(orbspin[:nocc] == 0)[0]
idxob = numpy.where(orbspin[:nocc] == 1)[0]
idxva = numpy.where(orbspin[nocc:] == 0)[0]
idxvb = numpy.where(orbspin[nocc:] == 1)[0]
if len(tx) == 2: # t1
t1 = numpy.zeros((nocc,nvir), dtype=t1a.dtype)
lib.takebak_2d(t1, t1a, idxoa, idxva)
lib.takebak_2d(t1, t1b, idxob, idxvb)
t1 = lib.tag_array(t1, orbspin=orbspin)
return t1
else:
t2 = numpy.zeros((nocc**2,nvir**2), dtype=t2aa.dtype)
idxoaa = idxoa[:,None] * nocc + idxoa
idxoab = idxoa[:,None] * nocc + idxob
idxoba = idxob[:,None] * nocc + idxoa
idxobb = idxob[:,None] * nocc + idxob
idxvaa = idxva[:,None] * nvir + idxva
idxvab = idxva[:,None] * nvir + idxvb
idxvba = idxvb[:,None] * nvir + idxva
idxvbb = idxvb[:,None] * nvir + idxvb
t2aa = t2aa.reshape(nocc_a*nocc_a,nvir_a*nvir_a)
t2ab = t2ab.reshape(nocc_a*nocc_b,nvir_a*nvir_b)
t2bb = t2bb.reshape(nocc_b*nocc_b,nvir_b*nvir_b)
lib.takebak_2d(t2, t2aa, idxoaa.ravel() , idxvaa.ravel() )
lib.takebak_2d(t2, t2bb, idxobb.ravel() , idxvbb.ravel() )
lib.takebak_2d(t2, t2ab, idxoab.ravel() , idxvab.ravel() )
lib.takebak_2d(t2, t2ab, idxoba.T.ravel(), idxvba.T.ravel())
abba = -t2ab
lib.takebak_2d(t2, abba, idxoab.ravel() , idxvba.T.ravel())
lib.takebak_2d(t2, abba, idxoba.T.ravel(), idxvab.ravel() )
t2 = lib.tag_array(t2, orbspin=orbspin)
return t2.reshape(nocc,nocc,nvir,nvir)
def __init__(self, mp, mo_coeff=None):
self.orbspin = None
if mo_coeff is None:
mo_coeff = mp.mo_coeff
mo_idx = mp.get_frozen_mask()
if getattr(mo_coeff, 'orbspin', None) is not None:
self.orbspin = mo_coeff.orbspin[mo_idx]
mo_coeff = lib.tag_array(mo_coeff[:,mo_idx], orbspin=self.orbspin)
self.mo_coeff = mo_coeff
else:
orbspin = scf.ghf.guess_orbspin(mo_coeff)
self.mo_coeff = mo_coeff = mo_coeff[:,mo_idx]
if not numpy.any(orbspin == -1):
self.orbspin = orbspin[mo_idx]
self.mo_coeff = lib.tag_array(mo_coeff, orbspin=self.orbspin)
def _make_eris_incore(mp, mo_coeff=None, ao2mofn=None, verbose=None):
eris = _PhysicistsERIs(mp, mo_coeff)
nocc = mp.nocc
nao, nmo = eris.mo_coeff.shape
nvir = nmo - nocc
orbspin = eris.orbspin
if callable(ao2mofn):
orbo = eris.mo_coeff[:,:nocc]
orbv = eris.mo_coeff[:,nocc:]
orbo = lib.tag_array(orbo, orbspin=orbspin)
eri = ao2mofn((orbo,orbv,orbo,orbv)).reshape(nocc,nvir,nocc,nvir)
else:
orboa = eris.mo_coeff[:nao//2,:nocc]
orbob = eris.mo_coeff[nao//2:,:nocc]
orbva = eris.mo_coeff[:nao//2,nocc:]
orbvb = eris.mo_coeff[nao//2:,nocc:]
if orbspin is None:
eri = ao2mo.kernel(mp._scf._eri, (orboa,orbva,orboa,orbva))
eri += ao2mo.kernel(mp._scf._eri, (orbob,orbvb,orbob,orbvb))
eri1 = ao2mo.kernel(mp._scf._eri, (orboa,orbva,orbob,orbvb))
eri += eri1
eri += eri1.T
eri = eri.reshape(nocc,nvir,nocc,nvir)
else:
co = orboa + orbob
cv = orbva + orbvb
eri = ao2mo.kernel(mp._scf._eri, (co,cv,co,cv)).reshape(nocc,nvir,nocc,nvir)
sym_forbid = (orbspin[:nocc,None] != orbspin[nocc:])
eri[sym_forbid,:,:] = 0
eri[:,:,sym_forbid] = 0
eris.oovv = eri.transpose(0,2,1,3) - eri.transpose(0,2,3,1)
return eris
def test_uhf_symm_get_occ(self):
pmol = n2sym.copy()
pmol.spin = 2
mf = scf.UHF(pmol).set(verbose = 0)
orbsym = numpy.array([[0 , 5 , 0 , 5 , 6 , 7 , 0 , 2 , 3 , 5 , 0 , 6 , 7 , 0 , 2 , 3 , 5 , 10, 11, 5],
[5 , 0 , 6 , 7 , 5 , 10, 11, 0 , 5 , 0 , 5 , 5 , 6 , 7 , 0 , 2 , 3 , 0 , 2 , 3]])
energy = numpy.array([[34, 2 , 54, 43, 42, 33, 20, 61, 29, 26, 62, 52, 13, 51, 18, 78, 85, 49, 84, 7],
[29, 26, 13, 54, 18, 78, 85, 49, 84, 62, 42, 74, 20, 61, 51, 34, 2 , 33, 52, 3]])
mf.irrep_nelec = {'A1g':7, 'A1u':3, 'E1ux':2, 'E1uy':2}
mo_coeff = lib.tag_array([numpy.eye(energy.size)]*2, orbsym=orbsym)
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[[1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
[0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]]))
mf.irrep_nelec = {'A1g':(5,2), 'A1u':(1,2)}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[[1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1]]))
mf.irrep_nelec = {'E1ux':2, 'E1uy':2}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[[0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1],
[0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1]]))
mf.irrep_nelec = {}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[[0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1],
[0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1]]))
def bcast_tagged_array(arr):
'''Broadcast big nparray or tagged array.'''
if comm.bcast(not isinstance(arr, numpy.ndarray)):
return comm.bcast(arr)
new_arr = bcast(arr)
if comm.bcast(isinstance(arr, lib.NPArrayWithTag)):
new_arr = lib.tag_array(new_arr)
if rank == 0:
kv = []
for k, v in arr.__dict__.items():
if isinstance(v, numpy.ndarray) and v.nbytes > 1e5:
kv.append((k, 'NPARRAY_TO_BCAST'))
else:
kv.append((k, v))
comm.bcast(kv)
else:
kv = comm.bcast(None)
new_arr.__dict__.update(kv)
for k, v in kv:
if v is 'NPARRAY_TO_BCAST':
new_arr.k = bcast(v)
if rank != 0:
arr = new_arr
return arr
def rotate_mo(self, mo_coeff, u, log=None):
mo = numpy.asarray((numpy.dot(mo_coeff[0], u[0]),
numpy.dot(mo_coeff[1], u[1])))
if self._scf.mol.symmetry:
orbsym = uhf_symm.get_orbsym(self._scf.mol, mo_coeff)
mo = lib.tag_array(mo, orbsym=orbsym)
return mo
def eig(self, fock, s):
e, c = self._eigh(fock, s)
if getattr(fock, 'focka', None) is not None:
mo_ea = numpy.einsum('pi,pi->i', c.conj(), fock.focka.dot(c)).real
mo_eb = numpy.einsum('pi,pi->i', c.conj(), fock.fockb.dot(c)).real
e = lib.tag_array(e, mo_ea=mo_ea, mo_eb=mo_eb)
return e, c
def get_roothaan_fock(focka_fockb, dma_dmb, s):
'''Roothaan's effective fock.
Ref. http://www-theor.ch.cam.ac.uk/people/ross/thesis/node15.html
======== ======== ====== =========
space closed open virtual
======== ======== ====== =========
closed Fc Fb Fc
open Fb Fc Fa
virtual Fc Fa Fc
======== ======== ====== =========
where Fc = (Fa + Fb) / 2
Returns:
Roothaan effective Fock matrix
'''
nao = s.shape[0]
focka, fockb = focka_fockb
dma, dmb = dma_dmb
fc = (focka + fockb) * .5
# Projector for core, open-shell, and virtual
pc = numpy.dot(dmb, s)
po = numpy.dot(dma-dmb, s)
pv = numpy.eye(nao) - numpy.dot(dma, s)
fock = reduce(numpy.dot, (pc.conj().T, fc, pc)) * .5
fock += reduce(numpy.dot, (po.conj().T, fc, po)) * .5
fock += reduce(numpy.dot, (pv.conj().T, fc, pv)) * .5
fock += reduce(numpy.dot, (po.conj().T, fockb, pc))
fock += reduce(numpy.dot, (po.conj().T, focka, pv))
fock += reduce(numpy.dot, (pv.conj().T, fc, pc))
fock = fock + fock.conj().T
fock = lib.tag_array(fock, focka=focka, fockb=fockb)
return fock
def test_canonicalize1(self):
numpy.random.seed(1)
f1 = numpy.random.random(mcr.mo_coeff.shape)
u1 = numpy.linalg.svd(f1)[0]
mo1 = numpy.dot(mcr.mo_coeff, u1)
mo1 = lib.tag_array(mo1, orbsym=mcr.mo_coeff.orbsym)
mo, ci, mo_e = mcr.canonicalize(mo1)
e1 = numpy.einsum('ji,jk,ki', mo, f1, mo)
self.assertAlmostEqual(e1, 44.2658681077, 7)
self.assertAlmostEqual(lib.finger(mo_e), 5.1364166175063097, 7)
mo, ci, mo_e = mcr.canonicalize(mo1, eris=mcr.ao2mo(mcr.mo_coeff))
e1 = numpy.einsum('ji,jk,ki', mo, f1, mo)
self.assertAlmostEqual(e1, 44.2658681077, 7)
self.assertAlmostEqual(lib.finger(mo_e), 4.1206025804989173, 7)
mcr1 = copy.copy(mcr)
mcr1.frozen = 2
mo, ci, mo_e = mcr1.canonicalize(mo1)
self.assertAlmostEqual(lib.finger(mo_e), 6.6030999409178577, 7)
mcr1.frozen = [0,1]
mo, ci, mo_e = mcr1.canonicalize(mo1)
self.assertAlmostEqual(lib.finger(mo_e), 6.6030999409178577, 7)
mcr1.frozen = [1,12]
mo, ci, mo_e = mcr1.canonicalize(mo1)
self.assertAlmostEqual(lib.finger(mo_e), 5.2182584355788162, 7)
def get_veff(ks, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
if getattr(dm, 'mo_coeff', None) is not None:
mo_coeff = dm.mo_coeff
mo_occ_a = (dm.mo_occ > 0).astype(numpy.double)
mo_occ_b = (dm.mo_occ ==2).astype(numpy.double)
dm = lib.tag_array(dm, mo_coeff=(mo_coeff,mo_coeff),
mo_occ=(mo_occ_a,mo_occ_b))
return uks.get_veff(ks, mol, dm, dm_last, vhf_last, hermi)
def get_veff(ks, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
kpts=None, kpts_band=None):
if getattr(dm, 'mo_coeff', None) is not None:
mo_coeff = dm.mo_coeff
mo_occ_a = [(x > 0).astype(np.double) for x in dm.mo_occ]
mo_occ_b = [(x ==2).astype(np.double) for x in dm.mo_occ]
dm = lib.tag_array(dm, mo_coeff=(mo_coeff,mo_coeff),
mo_occ=(mo_occ_a,mo_occ_b))
return kuks.get_veff(ks, cell, dm, dm_last, vhf_last, hermi, kpts, kpts_band)
def update_mo_(mf, mf1):
if mf.mo_energy is not None:
if isinstance(mf, scf.hf.RHF): # RHF
nao, nmo = mf.mo_coeff.shape
orbspin = get_ghf_orbspin(mf.mo_energy, mf.mo_occ, True)
mf1.mo_energy = numpy.empty(nmo*2)
mf1.mo_energy[orbspin==0] = mf.mo_energy
mf1.mo_energy[orbspin==1] = mf.mo_energy
mf1.mo_occ = numpy.empty(nmo*2)
mf1.mo_occ[orbspin==0] = mf.mo_occ > 0
mf1.mo_occ[orbspin==1] = mf.mo_occ == 2
mo_coeff = numpy.zeros((nao*2,nmo*2), dtype=mf.mo_coeff.dtype)
mo_coeff[:nao,orbspin==0] = mf.mo_coeff
mo_coeff[nao:,orbspin==1] = mf.mo_coeff
if getattr(mf.mo_coeff, 'orbsym', None) is not None:
orbsym = numpy.zeros_like(orbspin)
orbsym[orbspin==0] = mf.mo_coeff.orbsym
orbsym[orbspin==1] = mf.mo_coeff.orbsym
mo_coeff = lib.tag_array(mo_coeff, orbsym=orbsym)
mf1.mo_coeff = lib.tag_array(mo_coeff, orbspin=orbspin)
else: # UHF
nao, nmo = mf.mo_coeff[0].shape
orbspin = get_ghf_orbspin(mf.mo_energy, mf.mo_occ, False)
mf1.mo_energy = numpy.empty(nmo*2)
mf1.mo_energy[orbspin==0] = mf.mo_energy[0]
mf1.mo_energy[orbspin==1] = mf.mo_energy[1]
mf1.mo_occ = numpy.empty(nmo*2)
mf1.mo_occ[orbspin==0] = mf.mo_occ[0]
mf1.mo_occ[orbspin==1] = mf.mo_occ[1]
mo_coeff = numpy.zeros((nao*2,nmo*2), dtype=mf.mo_coeff[0].dtype)
mo_coeff[:nao,orbspin==0] = mf.mo_coeff[0]
mo_coeff[nao:,orbspin==1] = mf.mo_coeff[1]
if getattr(mf.mo_coeff[0], 'orbsym', None) is not None:
orbsym = numpy.zeros_like(orbspin)
orbsym[orbspin==0] = mf.mo_coeff[0].orbsym
orbsym[orbspin==1] = mf.mo_coeff[1].orbsym
mo_coeff = lib.tag_array(mo_coeff, orbsym=orbsym)
mf1.mo_coeff = lib.tag_array(mo_coeff, orbspin=orbspin)
return mf1
def get_veff(self, mol=None, dm=None, *args, **kwargs):
vhf = oldMF.get_veff(self, mol, dm, *args, **kwargs)
with_solvent = self.with_solvent
if not with_solvent.frozen:
with_solvent.epcm, with_solvent.vpcm = with_solvent.kernel(dm)
epcm, vpcm = with_solvent.epcm, with_solvent.vpcm
# NOTE: vpcm should not be added to vhf in this place. This is
# because vhf is used as the reference for direct_scf in the next
# iteration. If vpcm is added here, it may break direct SCF.
return lib.tag_array(vhf, epcm=epcm, vpcm=vpcm)
def test_rdm_complex(self):
mol = gto.M()
mol.verbose = 0
nocc = 6
nvir = 8
mf = scf.GHF(mol)
nmo = nocc + nvir
numpy.random.seed(1)
eri = (numpy.random.random((nmo,nmo,nmo,nmo)) +
numpy.random.random((nmo,nmo,nmo,nmo))* 1j - (.5+.5j))
eri = eri + eri.transpose(1,0,3,2).conj()
eri = eri + eri.transpose(2,3,0,1)
eri *= .1
def get_jk(mol, dm, *args,**kwargs):
vj = numpy.einsum('ijkl,lk->ij', eri, dm)
vk = numpy.einsum('ijkl,jk->il', eri, dm)
return vj, vk
def get_veff(mol, dm, *args, **kwargs):
vj, vk = get_jk(mol, dm)
return vj - vk
def ao2mofn(mos):
return eri
mf.get_jk = get_jk
mf.get_veff = get_veff
hcore = numpy.random.random((nmo,nmo)) * .2 + numpy.random.random((nmo,nmo))* .2j
hcore = hcore + hcore.T.conj() + numpy.diag(range(nmo))*2
mf.get_hcore = lambda *args: hcore
mf.get_ovlp = lambda *args: numpy.eye(nmo)
orbspin = numpy.zeros(nmo, dtype=int)
orbspin[1::2] = 1
mf.mo_coeff = lib.tag_array(numpy.eye(nmo) + 0j, orbspin=orbspin)
mf.mo_occ = numpy.zeros(nmo)
mf.mo_occ[:nocc] = 1
mf.e_tot = mf.energy_elec(mf.make_rdm1(), hcore)[0]
mycc = cc.GCCSD(mf)
eris = gccsd._make_eris_incore(mycc, mf.mo_coeff, ao2mofn)
mycc.ao2mo = lambda *args, **kwargs: eris
mycc.kernel(eris=eris)
mycc.solve_lambda(eris=eris)
dm1 = mycc.make_rdm1()
dm2 = mycc.make_rdm2()
e1 = numpy.einsum('ij,ji', hcore, dm1)
e1+= numpy.einsum('ijkl,ijkl', eri, dm2) * .5
self.assertAlmostEqual(e1, mycc.e_tot, 7)
self.assertAlmostEqual(abs(dm2-dm2.transpose(1,0,3,2).conj()).max(), 0, 9)
self.assertAlmostEqual(abs(dm2-dm2.transpose(2,3,0,1) ).max(), 0, 9)
self.assertAlmostEqual(abs(dm2+dm2.transpose(2,1,0,3) ).max(), 0, 9)
self.assertAlmostEqual(abs(dm2+dm2.transpose(0,3,2,1) ).max(), 0, 9)
def canonicalize(mf, mo_coeff, mo_occ, fock=None):
'''Canonicalization diagonalizes the Fock matrix within occupied, open,
virtual subspaces separatedly (without change occupancy).
'''
if getattr(fock, 'focka', None) is None:
dm = mf.make_rdm1(mo_coeff, mo_occ)
fock = mf.get_fock(dm=dm)
mo_e, mo_coeff = hf.canonicalize(mf, mo_coeff, mo_occ, fock)
fa, fb = fock.focka, fock.fockb
mo_ea = numpy.einsum('pi,pi->i', mo_coeff.conj(), fa.dot(mo_coeff)).real
mo_eb = numpy.einsum('pi,pi->i', mo_coeff.conj(), fb.dot(mo_coeff)).real
mo_e = lib.tag_array(mo_e, mo_ea=mo_ea, mo_eb=mo_eb)
return mo_e, mo_coeff
def test_multigrid_roks(self):
mf = dft.ROKS(cell_he)
mf.xc = 'lda,'
mo = dm_he[0]
nao = cell_he.nao
mo_occ = numpy.ones(nao)
dm1 = numpy.einsum('pi,i,qi->pq', mo, mo_occ, mo)
dm1 = lib.tag_array(numpy.array([dm1,dm1]), mo_coeff=mo,
mo_occ=mo_occ*2)
ref = mf.get_veff(cell_he, dm1)
out = multigrid.multigrid(mf).get_veff(cell_he, dm1)
self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9)
self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9)
self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 8)
def test_multigrid_kroks(self):
mf = dft.KROKS(cell_he)
mf.xc = 'lda,'
nao = cell_he.nao
mo = dm_he
mo_occ = numpy.ones((2,nao))
dm1 = numpy.einsum('kpi,ki,kqi->kpq', mo, mo_occ, mo)
dm1 = lib.tag_array(numpy.array([dm1,dm1]), mo_coeff=mo,
mo_occ=mo_occ*2)
ref = mf.get_veff(cell_he, dm1, kpts=kpts)
out = multigrid.multigrid(mf).get_veff(cell_he, dm1, kpts=kpts)
self.assertAlmostEqual(float(abs(ref-out).max()), 0, 9)
self.assertAlmostEqual(abs(ref.exc-out.exc).max(), 0, 9)
self.assertAlmostEqual(abs(ref.ecoul-out.ecoul).max(), 0, 9)
def get_veff(self, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
kpt=None, kpts_band=None):
if cell is None: cell = self.cell
if dm is None: dm = self.make_rdm1()
if kpt is None: kpt = self.kpt
if isinstance(dm, np.ndarray) and dm.ndim == 2:
dm = np.asarray((dm*.5,dm*.5))
if getattr(dm, 'mo_coeff', None) is not None:
mo_coeff = dm.mo_coeff
mo_occ_a = (dm.mo_occ > 0).astype(np.double)
mo_occ_b = (dm.mo_occ ==2).astype(np.double)
dm = lib.tag_array(dm, mo_coeff=(mo_coeff,mo_coeff),
mo_occ=(mo_occ_a,mo_occ_b))
vj, vk = self.get_jk(cell, dm, hermi, kpt, kpts_band)
vhf = vj[0] + vj[1] - vk
return vhf
def update_mo_(mf, mf1):
if mf.mo_energy is not None:
if isinstance(mf, scf.hf.RHF): # RHF/ROHF/KRHF/KROHF
mf1.mo_occ = mf.mo_occ
mf1.mo_coeff = mf.mo_coeff
mf1.mo_energy = mf.mo_energy
elif getattr(mf, 'kpts', None) is None: # UHF
mf1.mo_occ = mf.mo_occ[0] + mf.mo_occ[1]
mf1.mo_energy = mf.mo_energy[0]
mf1.mo_coeff = mf.mo_coeff[0]
if getattr(mf.mo_coeff[0], 'orbsym', None) is not None:
mf1.mo_coeff = lib.tag_array(mf1.mo_coeff, orbsym=mf.mo_coeff[0].orbsym)
else: # KUHF
mf1.mo_occ = [occa+occb for occa, occb in zip(*mf.mo_occ)]
mf1.mo_energy = mf.mo_energy[0]
mf1.mo_coeff = mf.mo_coeff[0]
return mf1
def get_fock(nmrobj, dm0=None, gauge_orig=None):
'''First order Fock matrix wrt external magnetic field'''
if dm0 is None: dm0 = nmrobj._scf.make_rdm1()
if gauge_orig is None: gauge_orig = nmrobj.gauge_orig
mol = nmrobj.mol
if gauge_orig is None:
log = logger.Logger(nmrobj.stdout, nmrobj.verbose)
log.debug('First-order GIAO Fock matrix')
mf = nmrobj._scf
ni = mf._numint
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
mem_now = lib.current_memory()[0]
max_memory = max(2000, mf.max_memory*.9-mem_now)
# Attach mo_coeff and mo_occ to improve get_vxc_giao efficiency
dm0 = lib.tag_array(dm0, mo_coeff=mf.mo_coeff, mo_occ=mf.mo_occ)
h1 = -get_vxc_giao(ni, mol, mf.grids, mf.xc, dm0,
max_memory=max_memory, verbose=nmrobj.verbose)
intor = mol._add_suffix('int2e_ig1')
if abs(hyb) > 1e-10:
vj, vk = rhf_nmr.get_jk(mol, dm0)
h1 += vj[0] + vj[1] - hyb * vk
if abs(omega) > 1e-10:
with mol.with_range_coulomb(omega):
h1 -= (alpha-hyb) * rhf_nmr.get_jk(mol, dm0)[1]
else:
vj = _vhf.direct_mapdm(intor, 'a4ij', 'lk->s1ij',
dm0, 3, mol._atm, mol._bas, mol._env)
h1 -= vj[0] + vj[1]
h1 -= .5 * mol.intor('int1e_giao_irjxp', 3)
h1 -= mol.intor_asymmetric('int1e_ignuc', 3)
if mol.has_ecp():
h1 -= mol.intor_asymmetric('ECPscalar_ignuc', 3)
h1 -= mol.intor('int1e_igkin', 3)
else:
with mol.with_common_origin(gauge_orig):
h1 = -.5 * mol.intor('int1e_cg_irxp', 3)
h1 = (h1, h1)
if nmrobj.chkfile:
lib.chkfile.dump(nmrobj.chkfile, 'nmr/h1', h1)
return h1
def update_mo_(mf, mf1):
_keys = mf._keys.union(mf1._keys)
mf1.__dict__.update(mf.__dict__)
mf1._keys = _keys
if mf.mo_energy is not None:
mf1.mo_energy = []
mf1.mo_occ = []
mf1.mo_coeff = []
nkpts = len(mf.kpts)
is_rhf = isinstance(mf, scf.hf.RHF)
for k in range(nkpts):
if is_rhf:
mo_a = mo_b = mf.mo_coeff[k]
ea = eb = mf.mo_energy[k]
occa = mf.mo_occ[k] > 0
occb = mf.mo_occ[k] == 2
orbspin = mol_addons.get_ghf_orbspin(ea, mf.mo_occ[k], True)
else:
mo_a = mf.mo_coeff[0][k]
mo_b = mf.mo_coeff[1][k]
ea = mf.mo_energy[0][k]
eb = mf.mo_energy[1][k]
occa = mf.mo_occ[0][k]
occb = mf.mo_occ[1][k]
orbspin = mol_addons.get_ghf_orbspin((ea, eb), (occa, occb), False)
nao, nmo = mo_a.shape
mo_energy = numpy.empty(nmo*2)
mo_energy[orbspin==0] = ea
mo_energy[orbspin==1] = eb
mo_occ = numpy.empty(nmo*2)
mo_occ[orbspin==0] = occa
mo_occ[orbspin==1] = occb
mo_coeff = numpy.zeros((nao*2,nmo*2), dtype=mo_a.dtype)
mo_coeff[:nao,orbspin==0] = mo_a
mo_coeff[nao:,orbspin==1] = mo_b
mo_coeff = lib.tag_array(mo_coeff, orbspin=orbspin)
mf1.mo_energy.append(mo_energy)
mf1.mo_occ.append(mo_occ)
mf1.mo_coeff.append(mo_coeff)
return mf1
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 test_rhf_symm_get_occ(self):
mf = scf.RHF(n2sym).set(verbose = 0)
orbsym = numpy.array([0 , 5, 0 , 5 , 6 , 7 , 0 , 2 , 3 , 5 , 0 , 6 , 7 , 0 , 2 , 3 , 5 , 10, 11, 5])
energy = numpy.array([34, 2, 54, 43, 42, 33, 20, 61, 29, 26, 62, 52, 13, 51, 18, 78, 85, 49, 84, 7])
mo_coeff = lib.tag_array(numpy.eye(energy.size), orbsym=orbsym)
mf.irrep_nelec = {'A1g':6, 'A1u':4, 'E1ux':2, 'E1uy':2}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[2, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2]))
mf.irrep_nelec = {'E1ux':2, 'E1uy':2}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[0, 2, 0, 0, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2]))
mf.irrep_nelec = {}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[0, 2, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2]))
mo_coeff = numpy.eye(energy.size)
self.assertTrue(numpy.allclose(mf.get_occ(energy),
[0, 2, 0, 0, 0, 0, 2, 0, 2, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2]))
def test_rohf_symm_get_occ(self):
pmol = n2sym.copy()
pmol.charge = 0
pmol.spin = 2
mf = scf.ROHF(pmol).set(verbose = 0)
orbsym = numpy.array([0 , 5, 0 , 5 , 6 , 7 , 0 , 2 , 3 , 5 , 0 , 6 , 7 , 0 , 2 , 3 , 5 , 10, 11, 5])
energy = numpy.array([34, 2, 54, 43, 42, 33, 20, 61, 29, 26, 62, 52, 13, 51, 18, 78, 85, 49, 84, 7])
mo_coeff = lib.tag_array(numpy.eye(energy.size), orbsym=orbsym)
mf.irrep_nelec = {'A1g':7, 'A1u':3, 'E1ux':2, 'E1uy':2}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[2, 2, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1]))
mf.irrep_nelec = {'E1ux':2, 'E1uy':2}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[0, 2, 0, 0, 2, 0, 2, 0, 1, 1, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2]))
mf.irrep_nelec = {}
self.assertTrue(numpy.allclose(mf.get_occ(energy, mo_coeff),
[0, 2, 0, 0, 0, 1, 2, 0, 1, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 2]))
mf1 = scf.RHF(mol).set(verbose=0).view(scf.hf_symm.ROHF)
self.assertTrue(numpy.allclose(mf1.get_occ(energy, mo_coeff),
[0 ,2 ,0 ,0 ,0 ,0 ,2 ,0 ,0 ,0 ,0 ,0 ,2 ,0 ,2 ,0 ,0 ,0 ,0 ,2]))
def get_veff(self, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
if mol is None: mol = self.mol
if dm is None: dm = self.make_rdm1()
if isinstance(dm, numpy.ndarray) and dm.ndim == 2:
dm = numpy.array((dm*.5, dm*.5))
if self._eri is not None or not self.direct_scf:
if getattr(dm, 'mo_coeff', None) is not None:
mo_coeff = dm.mo_coeff
mo_occ_a = (dm.mo_occ > 0).astype(numpy.double)
mo_occ_b = (dm.mo_occ ==2).astype(numpy.double)
dm = lib.tag_array(dm, mo_coeff=(mo_coeff,mo_coeff),
mo_occ=(mo_occ_a,mo_occ_b))
vj, vk = self.get_jk(mol, dm, hermi)
vhf = vj[0] + vj[1] - vk
else:
ddm = dm - numpy.asarray(dm_last)
vj, vk = self.get_jk(mol, ddm, hermi)
vhf = vj[0] + vj[1] - vk
vhf += numpy.asarray(vhf_last)
return vhf
def get_jk(mf_grad, mol=None, dm=None, hermi=0, with_j=True, with_k=True):
if mol is None: mol = mf_grad.mol
#if dm is None: dm = mf_grad.base.make_rdm1()
#TODO: dm has to be the SCF density matrix in this version. dm should be
# extended to any 1-particle density matrix
dm = mf_grad.base.make_rdm1()
with_df = mf_grad.base.with_df
auxmol = with_df.auxmol
if auxmol is None:
auxmol = df.addons.make_auxmol(with_df.mol, with_df.auxbasis)
pmol = mol + auxmol
ao_loc = mol.ao_loc
nbas = mol.nbas
nauxbas = auxmol.nbas
get_int3c_s1 = _int3c_wrapper(mol, auxmol, 'int3c2e', 's1')
get_int3c_s2 = _int3c_wrapper(mol, auxmol, 'int3c2e', 's2ij')
get_int3c_ip1 = _int3c_wrapper(mol, auxmol, 'int3c2e_ip1', 's1')
get_int3c_ip2 = _int3c_wrapper(mol, auxmol, 'int3c2e_ip2', 's2ij')
nao = mol.nao
naux = auxmol.nao
dms = numpy.asarray(dm)
out_shape = dms.shape[:-2] + (3, ) + dms.shape[-2:]
dms = dms.reshape(-1, nao, nao)
nset = dms.shape[0]
auxslices = auxmol.aoslice_by_atom()
aux_loc = auxmol.ao_loc
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = int(min(max(max_memory * .5e6 / 8 / (nao**2 * 3), 20), naux,
240))
ao_ranges = balance_partition(aux_loc, blksize)
if not with_k:
idx = numpy.arange(nao)
dm_tril = dms + dms.transpose(0, 2, 1)
dm_tril[:, idx, idx] *= .5
dm_tril = lib.pack_tril(dm_tril)
# (i,j|P)
rhoj = numpy.empty((nset, naux))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_s2((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
rhoj[:, p0:p1] = numpy.einsum('wp,nw->np', int3c, dm_tril)
int3c = None
# (P|Q)
int2c = auxmol.intor('int2c2e', aosym='s1')
rhoj = scipy.linalg.solve(int2c, rhoj.T, sym_pos=True).T
int2c = None
# (d/dX i,j|P)
vj = numpy.zeros((nset, 3, nao, nao))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip1((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
vj += numpy.einsum('xijp,np->nxij', int3c, rhoj[:, p0:p1])
int3c = None
if mf_grad.auxbasis_response:
# (i,j|d/dX P)
vjaux = numpy.empty((3, naux))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip2(
(0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
vjaux[:, p0:p1] = numpy.einsum('xwp,mw,np->xp', int3c, dm_tril,
rhoj[:, p0:p1])
int3c = None
# (d/dX P|Q)
int2c_e1 = auxmol.intor('int2c2e_ip1', aosym='s1')
vjaux -= numpy.einsum('xpq,mp,nq->xp', int2c_e1, rhoj, rhoj)
vjaux = [
-vjaux[:, p0:p1].sum(axis=1) for p0, p1 in auxslices[:, 2:]
]
vj = lib.tag_array(-vj.reshape(out_shape), aux=numpy.array(vjaux))
else:
vj = -vj.reshape(out_shape)
return vj, None
mo_coeff = mf_grad.base.mo_coeff
mo_occ = mf_grad.base.mo_occ
nmo = mo_occ.shape[-1]
if isinstance(mf_grad.base, scf.rohf.ROHF):
mo_coeff = numpy.vstack((mo_coeff, mo_coeff))
mo_occa = numpy.array(mo_occ > 0, dtype=numpy.double)
mo_occb = numpy.array(mo_occ == 2, dtype=numpy.double)
assert (mo_occa.sum() + mo_occb.sum() == mo_occ.sum())
mo_occ = numpy.vstack((mo_occa, mo_occb))
mo_coeff = numpy.asarray(mo_coeff).reshape(-1, nao, nmo)
mo_occ = numpy.asarray(mo_occ).reshape(-1, nmo)
rhoj = numpy.zeros((nset, naux))
f_rhok = lib.H5TmpFile()
orbo = []
for i in range(nset):
c = numpy.einsum('pi,i->pi', mo_coeff[i][:, mo_occ[i] > 0],
numpy.sqrt(mo_occ[i][mo_occ[i] > 0]))
nocc = c.shape[1]
orbo.append(c)
# (P|Q)
int2c = scipy.linalg.cho_factor(auxmol.intor('int2c2e', aosym='s1'))
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = max_memory * .5e6 / 8 / (naux * nao)
mol_ao_ranges = balance_partition(ao_loc, blksize)
nsteps = len(mol_ao_ranges)
for istep, (shl0, shl1, nd) in enumerate(mol_ao_ranges):
int3c = get_int3c_s1((0, nbas, shl0, shl1, 0, nauxbas))
p0, p1 = ao_loc[shl0], ao_loc[shl1]
rhoj += numpy.einsum('nlk,klp->np', dms[:, p0:p1], int3c)
for i in range(nset):
v = lib.einsum('ko,klp->plo', orbo[i], int3c)
v = scipy.linalg.cho_solve(int2c, v.reshape(naux, -1))
f_rhok['%s/%s' % (i, istep)] = v.reshape(naux, p1 - p0, -1)
int3c = v = None
rhoj = scipy.linalg.cho_solve(int2c, rhoj.T).T
int2c = None
def load(set_id, p0, p1):
nocc = orbo[set_id].shape[1]
buf = numpy.empty((p1 - p0, nocc, nao))
col1 = 0
for istep in range(nsteps):
dat = f_rhok['%s/%s' % (set_id, istep)][p0:p1]
col0, col1 = col1, col1 + dat.shape[1]
buf[:p1 - p0, :, col0:col1] = dat.transpose(0, 2, 1)
return buf
vj = numpy.zeros((nset, 3, nao, nao))
vk = numpy.zeros((nset, 3, nao, nao))
# (d/dX i,j|P)
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip1((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
vj += numpy.einsum('xijp,np->nxij', int3c, rhoj[:, p0:p1])
for i in range(nset):
tmp = lib.einsum('xijp,jo->xipo', int3c, orbo[i])
rhok = load(i, p0, p1)
vk[i] += lib.einsum('xipo,pok->xik', tmp, rhok)
tmp = rhok = None
int3c = None
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = int(min(max(max_memory * .5e6 / 8 / (nao * nocc), 20), naux))
rhok_oo = []
for i in range(nset):
nocc = orbo[i].shape[1]
tmp = numpy.empty((naux, nocc, nocc))
for p0, p1 in lib.prange(0, naux, blksize):
rhok = load(i, p0, p1)
tmp[p0:p1] = lib.einsum('pok,kr->por', rhok, orbo[i])
rhok_oo.append(tmp)
rhok = tmp = None
if mf_grad.auxbasis_response:
vjaux = numpy.zeros((3, naux))
vkaux = numpy.zeros((3, naux))
# (i,j|d/dX P)
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip2((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
int3c = int3c.transpose(0, 2, 1).reshape(3 * (p1 - p0), -1)
int3c = lib.unpack_tril(int3c)
int3c = int3c.reshape(3, p1 - p0, nao, nao)
vjaux[:, p0:p1] = numpy.einsum('xpij,mji,np->xp', int3c, dms,
rhoj[:, p0:p1])
for i in range(nset):
tmp = rhok_oo[i][p0:p1]
tmp = lib.einsum('por,ir->pio', tmp, orbo[i])
tmp = lib.einsum('pio,jo->pij', tmp, orbo[i])
vkaux[:, p0:p1] += lib.einsum('xpij,pij->xp', int3c, tmp)
int3c = tmp = None
# (d/dX P|Q)
int2c_e1 = auxmol.intor('int2c2e_ip1')
vjaux -= numpy.einsum('xpq,mp,nq->xp', int2c_e1, rhoj, rhoj)
for i in range(nset):
tmp = lib.einsum('pij,qij->pq', rhok_oo[i], rhok_oo[i])
vkaux -= numpy.einsum('xpq,pq->xp', int2c_e1, tmp)
vjaux = [-vjaux[:, p0:p1].sum(axis=1) for p0, p1 in auxslices[:, 2:]]
vkaux = [-vkaux[:, p0:p1].sum(axis=1) for p0, p1 in auxslices[:, 2:]]
vj = lib.tag_array(-vj.reshape(out_shape), aux=numpy.array(vjaux))
vk = lib.tag_array(-vk.reshape(out_shape), aux=numpy.array(vkaux))
else:
vj = -vj.reshape(out_shape)
vk = -vk.reshape(out_shape)
return vj, vk
def canonicalize(mf, mo_coeff, mo_occ, fock=None):
'''Canonicalization diagonalizes the UHF Fock matrix in occupied, virtual
subspaces separatedly (without change occupancy).
'''
mol = mf.mol
if not mol.symmetry:
return uhf.canonicalize(mf, mo_coeff, mo_occ, fock)
mo_occ = numpy.asarray(mo_occ)
assert (mo_occ.ndim == 2)
if fock is None:
dm = mf.make_rdm1(mo_coeff, mo_occ)
fock = mf.get_hcore() + mf.get_veff(mf.mol, dm)
occidxa = mo_occ[0] == 1
occidxb = mo_occ[1] == 1
viridxa = ~occidxa
viridxb = ~occidxb
mo = numpy.empty_like(mo_coeff)
mo_e = numpy.empty(mo_occ.shape)
if (getattr(mo_coeff, 'orbsym', None) is not None
or (getattr(mo_coeff[0], 'orbsym', None) is not None
and getattr(mo_coeff[1], 'orbsym', None) is not None)):
orbsyma, orbsymb = get_orbsym(mol, mo_coeff)
def eig_(fock, mo_coeff, idx, es, cs):
if numpy.count_nonzero(idx) > 0:
orb = mo_coeff[:, idx]
f1 = reduce(numpy.dot, (orb.T.conj(), fock, orb))
e, c = scipy.linalg.eigh(f1)
es[idx] = e
cs[:, idx] = numpy.dot(mo_coeff[:, idx], c)
for ir in set(orbsyma):
idx_ir = orbsyma == ir
eig_(fock[0], mo_coeff[0], idx_ir & occidxa, mo_e[0], mo[0])
eig_(fock[0], mo_coeff[0], idx_ir & viridxa, mo_e[0], mo[0])
for ir in set(orbsymb):
idx_ir = orbsymb == ir
eig_(fock[1], mo_coeff[1], idx_ir & occidxb, mo_e[1], mo[1])
eig_(fock[1], mo_coeff[1], idx_ir & viridxb, mo_e[1], mo[1])
else:
s = mf.get_ovlp()
def eig_(fock, mo_coeff, idx, es, cs):
if numpy.count_nonzero(idx) > 0:
orb = mo_coeff[:, idx]
f1 = reduce(numpy.dot, (orb.T.conj(), fock, orb))
e, c = scipy.linalg.eigh(f1)
es[idx] = e
c = numpy.dot(mo_coeff[:, idx], c)
cs[:, idx] = hf_symm._symmetrize_canonicalization_(mf, e, c, s)
eig_(fock[0], mo_coeff[0], occidxa, mo_e[0], mo[0])
eig_(fock[0], mo_coeff[0], viridxa, mo_e[0], mo[0])
eig_(fock[1], mo_coeff[1], occidxb, mo_e[1], mo[1])
eig_(fock[1], mo_coeff[1], viridxb, mo_e[1], mo[1])
orbsyma, orbsymb = get_orbsym(mol, mo, s, False)
mo = (lib.tag_array(mo[0],
orbsym=orbsyma), lib.tag_array(mo[1], orbsym=orbsymb))
return mo_e, mo
def spatial2spin(tx, orbspin, kconserv):
'''Convert T1/T2 of spatial orbital representation to T1/T2 of
spin-orbital representation
'''
if isinstance(tx, numpy.ndarray) and tx.ndim == 3:
# KRCCSD t1 amplitudes
return spatial2spin((tx, tx), orbspin, kconserv)
elif isinstance(tx, numpy.ndarray) and tx.ndim == 7:
# KRCCSD t2 amplitudes
t2aa = numpy.zeros_like(tx)
nkpts = t2aa.shape[2]
for ki, kj, ka in kpts_helper.loop_kkk(nkpts):
kb = kconserv[ki, ka, kj]
t2aa[ki, kj,
ka] = tx[ki, kj, ka] - tx[ki, kj, kb].transpose(0, 1, 3, 2)
return spatial2spin((t2aa, tx, t2aa), orbspin, kconserv)
elif len(tx) == 2: # KUCCSD t1
t1a, t1b = tx
nocc_a, nvir_a = t1a.shape[1:]
nocc_b, nvir_b = t1b.shape[1:]
else: # KUCCSD t2
t2aa, t2ab, t2bb = tx
nocc_a, nocc_b, nvir_a, nvir_b = t2ab.shape[3:]
nkpts = len(orbspin)
nocc = nocc_a + nocc_b
nvir = nvir_a + nvir_b
idxoa = [numpy.where(orbspin[k][:nocc] == 0)[0] for k in range(nkpts)]
idxob = [numpy.where(orbspin[k][:nocc] == 1)[0] for k in range(nkpts)]
idxva = [numpy.where(orbspin[k][nocc:] == 0)[0] for k in range(nkpts)]
idxvb = [numpy.where(orbspin[k][nocc:] == 1)[0] for k in range(nkpts)]
if len(tx) == 2: # t1
t1 = numpy.zeros((nkpts, nocc, nvir), dtype=t1a.dtype)
for k in range(nkpts):
lib.takebak_2d(t1[k], t1a[k], idxoa[k], idxva[k])
lib.takebak_2d(t1[k], t1b[k], idxob[k], idxvb[k])
t1 = lib.tag_array(t1, orbspin=orbspin)
return t1
else:
t2 = numpy.zeros((nkpts, nkpts, nkpts, nocc**2, nvir**2),
dtype=t2aa.dtype)
for ki, kj, ka in kpts_helper.loop_kkk(nkpts):
kb = kconserv[ki, ka, kj]
idxoaa = idxoa[ki][:, None] * nocc + idxoa[kj]
idxoab = idxoa[ki][:, None] * nocc + idxob[kj]
idxoba = idxob[kj][:, None] * nocc + idxoa[ki]
idxobb = idxob[ki][:, None] * nocc + idxob[kj]
idxvaa = idxva[ka][:, None] * nvir + idxva[kb]
idxvab = idxva[ka][:, None] * nvir + idxvb[kb]
idxvba = idxvb[kb][:, None] * nvir + idxva[ka]
idxvbb = idxvb[ka][:, None] * nvir + idxvb[kb]
tmp2aa = t2aa[ki, kj, ka].reshape(nocc_a * nocc_a, nvir_a * nvir_a)
tmp2bb = t2bb[ki, kj, ka].reshape(nocc_b * nocc_b, nvir_b * nvir_b)
tmp2ab = t2ab[ki, kj, ka].reshape(nocc_a * nocc_b, nvir_a * nvir_b)
lib.takebak_2d(t2[ki, kj, ka], tmp2aa, idxoaa.ravel(),
idxvaa.ravel())
lib.takebak_2d(t2[ki, kj, ka], tmp2bb, idxobb.ravel(),
idxvbb.ravel())
lib.takebak_2d(t2[ki, kj, ka], tmp2ab, idxoab.ravel(),
idxvab.ravel())
lib.takebak_2d(t2[kj, ki, kb], tmp2ab, idxoba.T.ravel(),
idxvba.T.ravel())
abba = -tmp2ab
lib.takebak_2d(t2[ki, kj, kb], abba, idxoab.ravel(),
idxvba.T.ravel())
lib.takebak_2d(t2[kj, ki, ka], abba, idxoba.T.ravel(),
idxvab.ravel())
t2 = t2.reshape(nkpts, nkpts, nkpts, nocc, nocc, nvir, nvir)
t2 = lib.tag_array(t2, orbspin=orbspin)
return t2
def get_jk(mf_grad,
mol=None,
dm=None,
hermi=0,
with_j=True,
with_k=True,
ishf=True):
t0 = (time.process_time(), time.time())
if mol is None: mol = mf_grad.mol
if dm is None: dm = mf_grad.base.make_rdm1()
with_df = mf_grad.base.with_df
auxmol = with_df.auxmol
if auxmol is None:
auxmol = df.addons.make_auxmol(with_df.mol, with_df.auxbasis)
pmol = mol + auxmol
ao_loc = mol.ao_loc
nbas = mol.nbas
nauxbas = auxmol.nbas
get_int3c_s1 = _int3c_wrapper(mol, auxmol, 'int3c2e', 's1')
get_int3c_s2 = _int3c_wrapper(mol, auxmol, 'int3c2e', 's2ij')
get_int3c_ip1 = _int3c_wrapper(mol, auxmol, 'int3c2e_ip1', 's1')
get_int3c_ip2 = _int3c_wrapper(mol, auxmol, 'int3c2e_ip2', 's2ij')
nao = mol.nao
naux = auxmol.nao
dms = numpy.asarray(dm)
out_shape = dms.shape[:-2] + (3, ) + dms.shape[-2:]
dms = dms.reshape(-1, nao, nao)
nset = dms.shape[0]
idx = numpy.arange(nao)
idx = idx * (idx + 1) // 2 + idx
dm_tril = dms + dms.transpose(0, 2, 1)
dm_tril = lib.pack_tril(dm_tril)
dm_tril[:, idx] *= .5
auxslices = auxmol.aoslice_by_atom()
aux_loc = auxmol.ao_loc
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = int(min(max(max_memory * .5e6 / 8 / (nao**2 * 3), 20), naux,
240))
ao_ranges = balance_partition(aux_loc, blksize)
if not with_k:
# (i,j|P)
rhoj = numpy.empty((nset, naux))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_s2((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
rhoj[:, p0:p1] = lib.einsum('wp,nw->np', int3c, dm_tril)
int3c = None
# (P|Q)
int2c = auxmol.intor('int2c2e', aosym='s1')
rhoj = scipy.linalg.solve(int2c, rhoj.T, sym_pos=True).T
int2c = None
# (d/dX i,j|P)
vj = numpy.zeros((nset, 3, nao, nao))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip1((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
vj += lib.einsum('xijp,np->nxij', int3c, rhoj[:, p0:p1])
int3c = None
if mf_grad.auxbasis_response:
# (i,j|d/dX P)
vjaux = numpy.empty((nset, nset, 3, naux))
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip2(
(0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
vjaux[:, :, :, p0:p1] = lib.einsum('xwp,mw,np->mnxp', int3c,
dm_tril, rhoj[:, p0:p1])
int3c = None
# (d/dX P|Q)
int2c_e1 = auxmol.intor('int2c2e_ip1', aosym='s1')
vjaux -= lib.einsum('xpq,mp,nq->mnxp', int2c_e1, rhoj, rhoj)
vjaux = numpy.array([
-vjaux[:, :, :, p0:p1].sum(axis=3)
for p0, p1 in auxslices[:, 2:]
])
if ishf:
vjaux = vjaux.sum((1, 2))
else:
vjaux = numpy.ascontiguousarray(vjaux.transpose(1, 2, 0, 3))
vj = lib.tag_array(-vj.reshape(out_shape), aux=numpy.array(vjaux))
else:
vj = -vj.reshape(out_shape)
logger.timer(mf_grad, 'df vj', *t0)
return vj, None
if hasattr(dm, 'mo_coeff') and hasattr(dm, 'mo_occ'):
mo_coeff = dm.mo_coeff
mo_occ = dm.mo_occ
elif ishf:
mo_coeff = mf_grad.base.mo_coeff
mo_occ = mf_grad.base.mo_occ
if isinstance(mf_grad.base, scf.rohf.ROHF):
mo_coeff = numpy.vstack((mo_coeff, mo_coeff))
mo_occa = numpy.array(mo_occ > 0, dtype=numpy.double)
mo_occb = numpy.array(mo_occ == 2, dtype=numpy.double)
assert (mo_occa.sum() + mo_occb.sum() == mo_occ.sum())
mo_occ = numpy.vstack((mo_occa, mo_occb))
else:
s0 = mol.intor('int1e_ovlp')
mo_occ = []
mo_coeff = []
for dm in dms:
sdms = reduce(lib.dot, (s0, dm, s0))
n, c = scipy.linalg.eigh(sdms, b=s0)
mo_occ.append(n)
mo_coeff.append(c)
mo_occ = numpy.stack(mo_occ, axis=0)
nmo = mo_occ.shape[-1]
mo_coeff = numpy.asarray(mo_coeff).reshape(-1, nao, nmo)
mo_occ = numpy.asarray(mo_occ).reshape(-1, nmo)
rhoj = numpy.zeros((nset, naux))
f_rhok = lib.H5TmpFile()
orbor = []
orbol = []
nocc = []
orbor_stack = numpy.zeros((nao, 0), dtype=mo_coeff.dtype, order='F')
orbol_stack = numpy.zeros((nao, 0), dtype=mo_coeff.dtype, order='F')
offs = 0
for i in range(nset):
idx = numpy.abs(mo_occ[i]) > 1e-8
nocc.append(numpy.count_nonzero(idx))
c = mo_coeff[i][:, idx]
orbol_stack = numpy.append(orbol_stack, c, axis=1)
orbol.append(orbol_stack[:, offs:offs + nocc[-1]])
cn = lib.einsum('pi,i->pi', c, mo_occ[i][idx])
orbor_stack = numpy.append(orbor_stack, cn, axis=1)
orbor.append(orbor_stack[:, offs:offs + nocc[-1]])
offs += nocc[-1]
# (P|Q)
int2c = scipy.linalg.cho_factor(auxmol.intor('int2c2e', aosym='s1'))
t1 = (time.process_time(), time.time())
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = max_memory * .5e6 / 8 / (naux * nao)
mol_ao_ranges = balance_partition(ao_loc, blksize)
nsteps = len(mol_ao_ranges)
t2 = t1
for istep, (shl0, shl1, nd) in enumerate(mol_ao_ranges):
int3c = get_int3c_s1((0, nbas, shl0, shl1, 0, nauxbas))
t2 = logger.timer_debug1(mf_grad, 'df grad intor (P|mn)', *t2)
p0, p1 = ao_loc[shl0], ao_loc[shl1]
for i in range(nset):
# MRH 05/21/2020: De-vectorize this because array contiguity -> parallel scaling
v = lib.dot(int3c.reshape(nao, -1, order='F').T,
orbor[i]).reshape(naux, (p1 - p0) * nocc[i])
t2 = logger.timer_debug1(mf_grad,
'df grad einsum (P|mn) u_ni N_i = v_Pmi',
*t2)
rhoj[i] += numpy.dot(v, orbol[i][p0:p1].ravel())
t2 = logger.timer_debug1(mf_grad,
'df grad einsum v_Pmi u_mi = rho_P', *t2)
v = scipy.linalg.cho_solve(int2c, v)
t2 = logger.timer_debug1(mf_grad,
'df grad cho_solve (P|Q) D_Qmi = v_Pmi',
*t2)
f_rhok['%s/%s' % (i, istep)] = v.reshape(naux, p1 - p0, -1)
t2 = logger.timer_debug1(
mf_grad,
'df grad cache D_Pmi (m <-> i transpose upon retrieval)', *t2)
int3c = v = None
rhoj = scipy.linalg.cho_solve(int2c, rhoj.T).T
int2c = None
t1 = logger.timer_debug1(
mf_grad, 'df grad vj and vk AO (P|Q) D_Q = (P|mn) D_mn solve', *t1)
def load(set_id, p0, p1):
buf = numpy.empty((p1 - p0, nocc[set_id], nao))
col1 = 0
for istep in range(nsteps):
dat = f_rhok['%s/%s' % (set_id, istep)][p0:p1]
col0, col1 = col1, col1 + dat.shape[1]
buf[:p1 - p0, :, col0:col1] = dat.transpose(0, 2, 1)
return buf
vj = numpy.zeros((nset, 3, nao, nao))
vk = numpy.zeros((nset, 3, nao, nao))
# (d/dX i,j|P)
fmmm = _ao2mo.libao2mo.AO2MOmmm_bra_nr_s1 # MO output index slower than AO output index; input AOs are asymmetric
fdrv = _ao2mo.libao2mo.AO2MOnr_e2_drv # comp and aux indices are slower
ftrans = _ao2mo.libao2mo.AO2MOtranse2_nr_s1 # input is not tril_packed
null = lib.c_null_ptr()
t2 = t1
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip1((0, nbas, 0, nbas, shl0,
shl1)).transpose(0, 3, 2,
1) # (P|mn'), row-major order
t2 = logger.timer_debug1(mf_grad, "df grad intor (P|mn')", *t2)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
for i in range(nset):
# MRH 05/21/2020: De-vectorize this because array contiguity -> parallel scaling
vj[i, 0] += numpy.dot(rhoj[i, p0:p1],
int3c[0].reshape(p1 - p0,
-1)).reshape(nao, nao).T
vj[i, 1] += numpy.dot(rhoj[i, p0:p1],
int3c[1].reshape(p1 - p0,
-1)).reshape(nao, nao).T
vj[i, 2] += numpy.dot(rhoj[i, p0:p1],
int3c[2].reshape(p1 - p0,
-1)).reshape(nao, nao).T
t2 = logger.timer_debug1(mf_grad,
"df grad einsum rho_P (P|mn') rho_P", *t2)
tmp = numpy.empty((3, p1 - p0, nocc[i], nao),
dtype=orbol_stack.dtype)
fdrv(
ftrans,
fmmm, # xPmn u_mi -> xPin
tmp.ctypes.data_as(ctypes.c_void_p),
int3c.ctypes.data_as(ctypes.c_void_p),
orbol[i].ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(3 * (p1 - p0)),
ctypes.c_int(nao),
(ctypes.c_int * 4)(0, nocc[i], 0, nao),
null,
ctypes.c_int(0))
t2 = logger.timer_debug1(mf_grad,
"df grad einsum (P|mn') u_mi = dg_Pin",
*t2)
rhok = load(i, p0, p1)
vk[i] += lib.einsum('xpoi,pok->xik', tmp, rhok)
t2 = logger.timer_debug1(mf_grad,
"df grad einsum D_Pim dg_Pin = v_ij", *t2)
rhok = tmp = None
int3c = None
t1 = logger.timer_debug1(mf_grad, 'df grad vj and vk AO (P|mn) D_P eval',
*t1)
if mf_grad.auxbasis_response:
# Cache (P|uv) D_ui c_vj. Must be include both upper and lower triangles
# over nset.
max_memory = mf_grad.max_memory - lib.current_memory()[0]
blksize = int(
min(max(max_memory * .5e6 / 8 / (nao * max(nocc)), 20), naux))
rhok_oo = []
for i, j in product(range(nset), repeat=2):
tmp = numpy.empty((naux, nocc[i], nocc[j]))
for p0, p1 in lib.prange(0, naux, blksize):
rhok = load(i, p0, p1).reshape((p1 - p0) * nocc[i], nao)
tmp[p0:p1] = lib.dot(rhok,
orbol[j]).reshape(p1 - p0, nocc[i],
nocc[j])
rhok_oo.append(tmp)
rhok = tmp = None
t1 = logger.timer_debug1(
mf_grad, 'df grad vj and vk aux d_Pim u_mj = d_Pij eval', *t1)
vjaux = numpy.zeros((nset, nset, 3, naux))
vkaux = numpy.zeros((nset, nset, 3, naux))
# (i,j|d/dX P)
t2 = t1
fmmm = _ao2mo.libao2mo.AO2MOmmm_bra_nr_s2 # MO output index slower than AO output index; input AOs are symmetric
fdrv = _ao2mo.libao2mo.AO2MOnr_e2_drv # comp and aux indices are slower
ftrans = _ao2mo.libao2mo.AO2MOtranse2_nr_s2 # input is tril_packed
null = lib.c_null_ptr()
for shl0, shl1, nL in ao_ranges:
int3c = get_int3c_ip2((0, nbas, 0, nbas, shl0, shl1)) # (i,j|P)
t2 = logger.timer_debug1(mf_grad, "df grad intor (P'|mn)", *t2)
p0, p1 = aux_loc[shl0], aux_loc[shl1]
drhoj = lib.dot(
int3c.transpose(0, 2, 1).reshape(3 * (p1 - p0), -1),
dm_tril.T).reshape(3, p1 - p0, -1) # xpij,mij->xpm
vjaux[:, :, :, p0:p1] = lib.einsum('xpm,np->mnxp', drhoj,
rhoj[:, p0:p1])
t2 = logger.timer_debug1(
mf_grad, "df grad einsum rho_P (P'|mn) D_mn = v_P", *t2)
tmp = [
numpy.empty((3, p1 - p0, nocc_i, nao), dtype=orbor_stack.dtype)
for nocc_i in nocc
]
assert (orbor_stack.flags.f_contiguous), '{} {}'.format(
orbor_stack.shape, orbor_stack.strides)
for orb, buf, nocc_i in zip(orbol, tmp, nocc):
fdrv(
ftrans,
fmmm, # gPmn u_ni -> gPim
buf.ctypes.data_as(ctypes.c_void_p),
int3c.ctypes.data_as(ctypes.c_void_p),
orb.ctypes.data_as(ctypes.c_void_p),
ctypes.c_int(3 * (p1 - p0)),
ctypes.c_int(nao),
(ctypes.c_int * 4)(0, nocc_i, 0, nao),
null,
ctypes.c_int(0))
int3c = [[
lib.dot(buf.reshape(-1, nao),
orb).reshape(3, p1 - p0, -1, norb)
for orb, norb in zip(orbor, nocc)
] for buf in tmp] # pim,mj,j -> pij
t2 = logger.timer_debug1(
mf_grad, "df grad einsum (P'|mn) u_mi u_nj N_j = v_Pmn", *t2)
for i, j in product(range(nset), repeat=2):
k = (i * nset) + j
tmp = rhok_oo[k][p0:p1]
vkaux[i, j, :, p0:p1] += lib.einsum('xpij,pij->xp',
int3c[i][j], tmp)
t2 = logger.timer_debug1(mf_grad,
"df grad einsum d_Pij v_Pij = v_P",
*t2)
int3c = tmp = None
t1 = logger.timer_debug1(mf_grad, "df grad vj and vk aux (P'|mn) eval",
*t1)
# (d/dX P|Q)
int2c_e1 = auxmol.intor('int2c2e_ip1')
vjaux -= lib.einsum('xpq,mp,nq->mnxp', int2c_e1, rhoj, rhoj)
for i, j in product(range(nset), repeat=2):
k = (i * nset) + j
l = (j * nset) + i
tmp = lib.einsum('pij,qji->pq', rhok_oo[k], rhok_oo[l])
vkaux[i, j] -= lib.einsum('xpq,pq->xp', int2c_e1, tmp)
t1 = logger.timer_debug1(mf_grad, "df grad vj and vk aux (P'|Q) eval",
*t1)
vjaux = numpy.array([
-vjaux[:, :, :, p0:p1].sum(axis=3) for p0, p1 in auxslices[:, 2:]
])
vkaux = numpy.array([
-vkaux[:, :, :, p0:p1].sum(axis=3) for p0, p1 in auxslices[:, 2:]
])
if ishf:
vjaux = vjaux.sum((1, 2))
idx = numpy.array(list(range(nset))) * (nset + 1)
vkaux = vkaux.reshape((nset**2, 3, mol.natm))[idx, :, :].sum(0)
else:
vjaux = numpy.ascontiguousarray(vjaux.transpose(1, 2, 0, 3))
vkaux = numpy.ascontiguousarray(vkaux.transpose(1, 2, 0, 3))
vj = lib.tag_array(-vj.reshape(out_shape), aux=numpy.array(vjaux))
vk = lib.tag_array(-vk.reshape(out_shape), aux=numpy.array(vkaux))
else:
vj = -vj.reshape(out_shape)
vk = -vk.reshape(out_shape)
logger.timer(mf_grad, 'df grad vj and vk', *t0)
return vj, vk
def _make_eris_incore(cc, mo_coeff=None):
from pyscf.pbc import tools
from pyscf.pbc.cc.ccsd import _adjust_occ
log = logger.Logger(cc.stdout, cc.verbose)
cput0 = (time.clock(), time.time())
eris = gccsd._PhysicistsERIs()
cell = cc._scf.cell
kpts = cc.kpts
nkpts = cc.nkpts
nocc = cc.nocc
nmo = cc.nmo
nvir = nmo - nocc
eris.nocc = nocc
#if any(nocc != numpy.count_nonzero(cc._scf.mo_occ[k] > 0) for k in range(nkpts)):
# raise NotImplementedError('Different occupancies found for different k-points')
if mo_coeff is None:
mo_coeff = cc.mo_coeff
nao = mo_coeff[0].shape[0]
dtype = mo_coeff[0].dtype
moidx = get_frozen_mask(cc)
nocc_per_kpt = numpy.asarray(get_nocc(cc, per_kpoint=True))
nmo_per_kpt = numpy.asarray(get_nmo(cc, per_kpoint=True))
padded_moidx = []
for k in range(nkpts):
kpt_nocc = nocc_per_kpt[k]
kpt_nvir = nmo_per_kpt[k] - kpt_nocc
kpt_padded_moidx = numpy.concatenate(
(numpy.ones(kpt_nocc, dtype=numpy.bool),
numpy.zeros(nmo - kpt_nocc - kpt_nvir, dtype=numpy.bool),
numpy.ones(kpt_nvir, dtype=numpy.bool)))
padded_moidx.append(kpt_padded_moidx)
eris.mo_coeff = []
eris.orbspin = []
# Generate the molecular orbital coefficients with the frozen orbitals masked.
# Each MO is tagged with orbspin, a list of 0's and 1's that give the overall
# spin of each MO.
#
# Here we will work with two index arrays; one is for our original (small) moidx
# array while the next is for our new (large) padded array.
for k in range(nkpts):
kpt_moidx = moidx[k]
kpt_padded_moidx = padded_moidx[k]
mo = numpy.zeros((nao, nmo), dtype=dtype)
mo[:, kpt_padded_moidx] = mo_coeff[k][:, kpt_moidx]
if getattr(mo_coeff[k], 'orbspin', None) is not None:
orbspin_dtype = mo_coeff[k].orbspin[kpt_moidx].dtype
orbspin = numpy.zeros(nmo, dtype=orbspin_dtype)
orbspin[kpt_padded_moidx] = mo_coeff[k].orbspin[kpt_moidx]
mo = lib.tag_array(mo, orbspin=orbspin)
eris.orbspin.append(orbspin)
# FIXME: What if the user freezes all up spin orbitals in
# an RHF calculation? The number of electrons will still be
# even.
else: # guess orbital spin - assumes an RHF calculation
assert (numpy.count_nonzero(kpt_moidx) % 2 == 0)
orbspin = numpy.zeros(mo.shape[1], dtype=int)
orbspin[1::2] = 1
mo = lib.tag_array(mo, orbspin=orbspin)
eris.orbspin.append(orbspin)
eris.mo_coeff.append(mo)
# Re-make our fock MO matrix elements from density and fock AO
dm = cc._scf.make_rdm1(cc.mo_coeff, cc.mo_occ)
with lib.temporary_env(cc._scf, exxdiv=None):
# _scf.exxdiv affects eris.fock. HF exchange correction should be
# excluded from the Fock matrix.
vhf = cc._scf.get_veff(cell, dm)
fockao = cc._scf.get_hcore() + vhf
eris.fock = numpy.asarray([
reduce(numpy.dot, (mo.T.conj(), fockao[k], mo))
for k, mo in enumerate(eris.mo_coeff)
])
eris.e_hf = cc._scf.energy_tot(dm=dm, vhf=vhf)
eris.mo_energy = [eris.fock[k].diagonal().real for k in range(nkpts)]
# Add HFX correction in the eris.mo_energy to improve convergence in
# CCSD iteration. It is useful for the 2D systems since their occupied and
# the virtual orbital energies may overlap which may lead to numerical
# issue in the CCSD iterations.
# FIXME: Whether to add this correction for other exxdiv treatments?
# Without the correction, MP2 energy may be largely off the correct value.
madelung = tools.madelung(cell, kpts)
eris.mo_energy = [
_adjust_occ(mo_e, nocc, -madelung)
for k, mo_e in enumerate(eris.mo_energy)
]
# Get location of padded elements in occupied and virtual space.
nocc_per_kpt = get_nocc(cc, per_kpoint=True)
nonzero_padding = padding_k_idx(cc, kind="joint")
# Check direct and indirect gaps for possible issues with CCSD convergence.
mo_e = [eris.mo_energy[kp][nonzero_padding[kp]] for kp in range(nkpts)]
mo_e = numpy.sort([y for x in mo_e for y in x]) # Sort de-nested array
gap = mo_e[numpy.sum(nocc_per_kpt)] - mo_e[numpy.sum(nocc_per_kpt) - 1]
if gap < 1e-5:
logger.warn(
cc, 'H**O-LUMO gap %s too small for KCCSD. '
'May cause issues in convergence.', gap)
kconserv = kpts_helper.get_kconserv(cell, kpts)
if getattr(mo_coeff[0], 'orbspin', None) is None:
# The bottom nao//2 coefficients are down (up) spin while the top are up (down).
mo_a_coeff = [mo[:nao // 2] for mo in eris.mo_coeff]
mo_b_coeff = [mo[nao // 2:] for mo in eris.mo_coeff]
eri = numpy.empty((nkpts, nkpts, nkpts, nmo, nmo, nmo, nmo),
dtype=numpy.complex128)
fao2mo = cc._scf.with_df.ao2mo
for kp, kq, kr in kpts_helper.loop_kkk(nkpts):
ks = kconserv[kp, kq, kr]
eri_kpt = fao2mo((mo_a_coeff[kp], mo_a_coeff[kq], mo_a_coeff[kr],
mo_a_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt += fao2mo((mo_b_coeff[kp], mo_b_coeff[kq], mo_b_coeff[kr],
mo_b_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt += fao2mo((mo_a_coeff[kp], mo_a_coeff[kq], mo_b_coeff[kr],
mo_b_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt += fao2mo((mo_b_coeff[kp], mo_b_coeff[kq], mo_a_coeff[kr],
mo_a_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt = eri_kpt.reshape(nmo, nmo, nmo, nmo)
eri[kp, kq, kr] = eri_kpt
else:
mo_a_coeff = [mo[:nao // 2] + mo[nao // 2:] for mo in eris.mo_coeff]
eri = numpy.empty((nkpts, nkpts, nkpts, nmo, nmo, nmo, nmo),
dtype=numpy.complex128)
fao2mo = cc._scf.with_df.ao2mo
for kp, kq, kr in kpts_helper.loop_kkk(nkpts):
ks = kconserv[kp, kq, kr]
eri_kpt = fao2mo((mo_a_coeff[kp], mo_a_coeff[kq], mo_a_coeff[kr],
mo_a_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt[(eris.orbspin[kp][:, None] !=
eris.orbspin[kq]).ravel()] = 0
eri_kpt[:,
(eris.orbspin[kr][:,
None] != eris.orbspin[ks]).ravel()] = 0
eri_kpt = eri_kpt.reshape(nmo, nmo, nmo, nmo)
eri[kp, kq, kr] = eri_kpt
# Check some antisymmetrized properties of the integrals
if DEBUG:
check_antisymm_3412(cc, cc.kpts, eri)
# Antisymmetrizing (pq|rs)-(ps|rq), where the latter integral is equal to
# (rq|ps); done since we aren't tracking the kpoint of orbital 's'
eri = eri - eri.transpose(2, 1, 0, 5, 4, 3, 6)
# Chemist -> physics notation
eri = eri.transpose(0, 2, 1, 3, 5, 4, 6)
# Set the various integrals
eris.dtype = eri.dtype
eris.oooo = eri[:, :, :, :nocc, :nocc, :nocc, :nocc].copy() / nkpts
eris.ooov = eri[:, :, :, :nocc, :nocc, :nocc, nocc:].copy() / nkpts
eris.ovoo = eri[:, :, :, :nocc, nocc:, :nocc, :nocc].copy() / nkpts
eris.oovv = eri[:, :, :, :nocc, :nocc, nocc:, nocc:].copy() / nkpts
eris.ovov = eri[:, :, :, :nocc, nocc:, :nocc, nocc:].copy() / nkpts
eris.ovvv = eri[:, :, :, :nocc, nocc:, nocc:, nocc:].copy() / nkpts
eris.vvvv = eri[:, :, :, nocc:, nocc:, nocc:, nocc:].copy() / nkpts
log.timer('CCSD integral transformation', *cput0)
return eris
def kernel(ot,
oneCDMs_amo,
twoCDM_amo,
mo_coeff,
ncore,
ncas,
max_memory=20000,
hermi=1,
veff2_mo=None,
paaa_only=False):
''' Get the 1- and 2-body effective potential from MC-PDFT. Eventually I'll be able to specify
mo slices for the 2-body part
Args:
ot : an instance of otfnal class
oneCDMs_amo : ndarray of shape (2, ncas, ncas)
containing spin-separated one-body density matrices
twoCDM_amo : ndarray of shape (ncas, ncas, ncas, ncas)
containing spin-summed two-body cumulant density matrix in an active space
ao2amo : ndarray of shape (nao, ncas)
containing molecular orbital coefficients for active-space orbitals
Kwargs:
max_memory : int or float
maximum cache size in MB
default is 20000
hermi : int
1 if 1CDMs are assumed hermitian, 0 otherwise
Returns : float
The MC-PDFT on-top exchange-correlation energy
'''
if veff2_mo is not None:
raise NotImplementedError(
'Molecular orbital slices for the two-body part')
nocc = ncore + ncas
ni, xctype, dens_deriv = ot._numint, ot.xctype, ot.dens_deriv
norbs_ao = mo_coeff.shape[0]
mo_core = mo_coeff[:, :ncore]
ao2amo = mo_coeff[:, ncore:nocc]
npair = norbs_ao * (norbs_ao + 1) // 2
veff1 = np.zeros((norbs_ao, norbs_ao), dtype=oneCDMs_amo.dtype)
veff2 = _ERIS(ot.mol,
mo_coeff,
ncore,
ncas,
paaa_only=paaa_only,
verbose=ot.verbose,
stdout=ot.stdout)
t0 = (time.clock(), time.time())
dm_core = mo_core @ mo_core.T
dm_cas = np.dot(ao2amo, np.dot(oneCDMs_amo, ao2amo.T)).transpose(1, 0, 2)
dm1s = dm_cas + dm_core[None, :, :]
dm_core *= 2
# Can't trust that NOs are the same for alpha and beta. Have to do this explicitly here
# Begin tag block: dm_core
imo_occ = np.ones(ncore, dtype=dm_core.dtype) * 2.0
dm_core = tag_array(dm_core, mo_coeff=mo_core, mo_occ=imo_occ)
# Begin tag block: dm_cas
amo_occ = np.zeros((2, ncas), dtype=dm_cas.dtype)
amo_coeff = np.stack([ao2amo.copy(), ao2amo.copy()], axis=0)
for i in range(2):
amo_occ[i], ua = linalg.eigh(oneCDMs_amo[i])
amo_coeff[i] = amo_coeff[i] @ ua
dm_cas = tag_array(dm_cas, mo_coeff=amo_coeff, mo_occ=amo_occ)
# Begin tag block: dm1s
mo_occ = np.zeros((2, nocc), dtype=dm1s.dtype)
mo_occ[:, :ncore] = 1.0
mo_occ[:, ncore:nocc] = amo_occ
tag_coeff = np.stack(
(mo_coeff[:, :nocc].copy(), mo_coeff[:, :nocc].copy()), axis=0)
tag_coeff[:, :, ncore:nocc] = amo_coeff
dm1s = tag_array(dm1s, mo_coeff=tag_coeff, mo_occ=mo_occ)
# End tag block
make_rho_c, nset_c, nao_c = ni._gen_rho_evaluator(ot.mol, dm_core, hermi)
make_rho_a, nset_a, nao_a = ni._gen_rho_evaluator(ot.mol, dm_cas, hermi)
make_rho, nset, nao = ni._gen_rho_evaluator(ot.mol, dm1s, hermi)
gc.collect()
remaining_floats = (max_memory - current_memory()[0]) * 1e6 / 8
nderiv_rho = (1, 4, 10)[dens_deriv] # ?? for meta-GGA
nderiv_Pi = (1, 4)[ot.Pi_deriv]
ncols_v2 = norbs_ao * ncas + ncas**2 if paaa_only else 2 * norbs_ao * ncas
ncols = 1 + nderiv_rho * (5 + norbs_ao * 2) + nderiv_Pi * (1 + ncols_v2)
pdft_blksize = int(
remaining_floats /
(ncols * BLKSIZE)) * BLKSIZE # something something indexing
if ot.grids.coords is None:
ot.grids.build(with_non0tab=True)
ngrids = ot.grids.coords.shape[0]
pdft_blksize = max(BLKSIZE, min(pdft_blksize, ngrids, BLKSIZE * 1200))
logger.debug(
ot,
'{} MB used of {} available; block size of {} chosen for grid with {} points'
.format(current_memory()[0], max_memory, pdft_blksize, ngrids))
shls_slice = (0, ot.mol.nbas)
ao_loc = ot.mol.ao_loc_nr()
for ao, mask, weight, coords in ni.block_loop(ot.mol,
ot.grids,
norbs_ao,
dens_deriv,
max_memory,
blksize=pdft_blksize):
rho = np.asarray([make_rho(i, ao, mask, xctype) for i in range(2)])
rho_a = np.asarray([make_rho_a(i, ao, mask, xctype) for i in range(2)])
rho_c = make_rho_c(0, ao, mask, xctype)
t0 = logger.timer(ot, 'untransformed densities (core and total)', *t0)
Pi = get_ontop_pair_density(ot, rho, ao, dm1s, twoCDM_amo, ao2amo,
dens_deriv, mask)
t0 = logger.timer(ot, 'on-top pair density calculation', *t0)
eot, vrho, vPi = ot.eval_ot(rho, Pi, weights=weight)
t0 = logger.timer(ot, 'effective potential kernel calculation', *t0)
veff1 += ot.get_veff_1body(rho,
Pi,
ao,
weight,
non0tab=mask,
shls_slice=shls_slice,
ao_loc=ao_loc,
hermi=1,
kern=vrho)
t0 = logger.timer(ot, '1-body effective potential calculation', *t0)
#ao[:,:,:] = np.tensordot (ao, mo_coeff, axes=1)
#t0 = logger.timer (ot, 'ao2mo grid points', *t0)
veff2._accumulate(ot, rho, Pi, ao, weight, rho_c, rho_a, vPi, mask,
shls_slice, ao_loc)
t0 = logger.timer(ot, '2-body effective potential calculation', *t0)
veff2._finalize()
t0 = logger.timer(ot, 'Finalizing 2-body effective potential calculation',
*t0)
return veff1, veff2
def rotate_mo(self, mo, u, log=None):
'''Rotate orbitals with the given unitary matrix'''
mo = mc1step.CASSCF.rotate_mo(self, mo, u, log)
mo = lib.tag_array(mo, orbsym=self.mo_coeff.orbsym)
return mo
def get_veff(ks, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
'''Coulomb + XC functional for UKS. See pyscf/dft/rks.py
:func:`get_veff` fore more details.
'''
if mol is None: mol = ks.mol
if dm is None: dm = ks.make_rdm1()
if not isinstance(dm, numpy.ndarray):
dm = numpy.asarray(dm)
if dm.ndim == 2: # RHF DM
dm = numpy.asarray((dm * .5, dm * .5))
ground_state = (dm.ndim == 3 and dm.shape[0] == 2)
t0 = (logger.process_clock(), logger.perf_counter())
if ks.grids.coords is None:
ks.grids.build(with_non0tab=True)
if ks.small_rho_cutoff > 1e-20 and ground_state:
ks.grids = rks.prune_small_rho_grids_(ks, mol, dm[0] + dm[1],
ks.grids)
t0 = logger.timer(ks, 'setting up grids', *t0)
if ks.nlc != '':
if ks.nlcgrids.coords is None:
ks.nlcgrids.build(with_non0tab=True)
if ks.small_rho_cutoff > 1e-20 and ground_state:
ks.nlcgrids = rks.prune_small_rho_grids_(
ks, mol, dm[0] + dm[1], ks.nlcgrids)
t0 = logger.timer(ks, 'setting up nlc grids', *t0)
ni = ks._numint
if hermi == 2: # because rho = 0
n, exc, vxc = (0, 0), 0, 0
else:
max_memory = ks.max_memory - lib.current_memory()[0]
n, exc, vxc = ni.nr_uks(mol,
ks.grids,
ks.xc,
dm,
max_memory=max_memory)
if ks.nlc:
assert 'VV10' in ks.nlc.upper()
_, enlc, vnlc = ni.nr_rks(mol,
ks.nlcgrids,
ks.xc + '__' + ks.nlc,
dm[0] + dm[1],
max_memory=max_memory)
exc += enlc
vxc += vnlc
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(ks.xc, spin=mol.spin)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vk = None
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vj', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj = ks.get_j(mol, ddm[0] + ddm[1], hermi)
vj += vhf_last.vj
else:
vj = ks.get_j(mol, dm[0] + dm[1], hermi)
vxc += vj
else:
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vk', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj, vk = ks.get_jk(mol, ddm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, ddm, hermi, omega)
vklr *= (alpha - hyb)
vk += vklr
vj = vj[0] + vj[1] + vhf_last.vj
vk += vhf_last.vk
else:
vj, vk = ks.get_jk(mol, dm, hermi)
vj = vj[0] + vj[1]
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, dm, hermi, omega)
vklr *= (alpha - hyb)
vk += vklr
vxc += vj - vk
if ground_state:
exc -= (numpy.einsum('ij,ji', dm[0], vk[0]).real +
numpy.einsum('ij,ji', dm[1], vk[1]).real) * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm[0] + dm[1], vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=vj, vk=vk)
return vxc
def canonicalize(mc,
mo_coeff=None,
ci=None,
eris=None,
sort=False,
cas_natorb=False,
casdm1=None,
verbose=logger.NOTE,
with_meta_lowdin=WITH_META_LOWDIN):
'''Canonicalized CASCI/CASSCF orbitals of effecitive Fock matrix and
update CI coefficients accordingly.
Effective Fock matrix is built with one-particle density matrix (see
also :func:`mcscf.casci.get_fock`). For state-average CASCI/CASSCF object,
the canonicalized orbitals are based on the state-average density matrix.
To obtain canonicalized orbitals for an individual state, you need to pass
"casdm1" of the specific state to this function.
Args:
mc: a CASSCF/CASCI object or RHF object
Kwargs:
mo_coeff (ndarray): orbitals that span the core, active and external
space.
ci (ndarray): CI coefficients (or objects to represent the CI
wavefunctions in DMRG/QMC-MCSCF calculations).
eris: Integrals for the MCSCF object. Input this object to reduce the
overhead of computing integrals. It can be generated by
:func:`mc.ao2mo` method.
sort (bool): Whether the canonicalized orbitals are sorted based on
the orbital energy (diagonal part of the effective Fock matrix)
within each subspace (core, active, external). If point group
symmetry is not available in the system, orbitals are always
sorted. When point group symmetry is available, sort=False will
preserve the symmetry label of input orbitals and only sort the
orbitals in each symmetry sector. sort=True will reorder all
orbitals over all symmetry sectors in each subspace and the
symmetry labels may be changed.
cas_natorb (bool): Whether to transform active orbitals to natual
orbitals. If enabled, the output orbitals in active space are
transformed to natural orbitals and CI coefficients are updated
accordingly.
casdm1 (ndarray): 1-particle density matrix in active space. This
density matrix is used to build effective fock matrix. Without
input casdm1, the density matrix is computed with the input ci
coefficients/object. If neither ci nor casdm1 were given, density
matrix is computed by :func:`mc.fcisolver.make_rdm1` method. For
state-average CASCI/CASCF calculation, this results in a set of
canonicalized orbitals of state-average effective Fock matrix.
To canonicalize the orbitals for one particular state, you can
assign the density matrix of that state to the kwarg casdm1.
Returns:
A tuple, (natural orbitals, CI coefficients, orbital energies)
The orbital energies are the diagonal terms of effective Fock matrix.
'''
from pyscf.lo import orth
from pyscf.tools import dump_mat
from pyscf.mcscf import addons
log = logger.new_logger(mc, verbose)
if mo_coeff is None: mo_coeff = mc.mo_coeff
if ci is None: ci = mc.ci
if casdm1 is None:
if (isinstance(ci, (list, tuple)) and
not isinstance(mc.fcisolver, addons.StateAverageFCISolver)):
log.warn('Mulitple states found in CASCI solver. First state is '
'used to compute the natural orbitals in active space.')
casdm1 = mc.fcisolver.make_rdm1(ci[0], mc.ncas, mc.nelecas)
else:
casdm1 = mc.fcisolver.make_rdm1(ci, mc.ncas, mc.nelecas)
ncore = mc.ncore
nocc = ncore + mc.ncas
nmo = mo_coeff.shape[1]
fock_ao = mc.get_fock(mo_coeff, ci, eris, casdm1, verbose)
if cas_natorb:
mo_coeff1, ci, occ = mc.cas_natorb(mo_coeff, ci, eris, sort, casdm1,
verbose, with_meta_lowdin)
else:
# Keep the active space unchanged by default. The rotation in active space
# may cause problem for external CI solver eg DMRG.
mo_coeff1 = mo_coeff.copy()
log.info('Density matrix diagonal elements %s', casdm1.diagonal())
fock = reduce(numpy.dot, (mo_coeff1.T, fock_ao, mo_coeff1))
mo_energy = fock.diagonal().copy()
mask = numpy.ones(nmo, dtype=bool)
frozen = getattr(mc, 'frozen', None)
if frozen is not None:
if isinstance(frozen, (int, numpy.integer)):
mask[:frozen] = False
else:
mask[frozen] = False
core_idx = numpy.where(mask[:ncore])[0]
vir_idx = numpy.where(mask[nocc:])[0] + nocc
if getattr(mo_coeff, 'orbsym', None) is not None:
orbsym = mo_coeff.orbsym
else:
orbsym = numpy.zeros(nmo, dtype=int)
if len(core_idx) > 0:
# note the last two args of ._eig for mc1step_symm
# mc._eig function is called to handle symmetry adapated fock
w, c1 = mc._eig(fock[core_idx[:, None], core_idx], 0, ncore,
orbsym[core_idx])
if sort:
idx = numpy.argsort(w.round(9), kind='mergesort')
w = w[idx]
c1 = c1[:, idx]
orbsym[core_idx] = orbsym[core_idx][idx]
mo_coeff1[:, core_idx] = numpy.dot(mo_coeff1[:, core_idx], c1)
mo_energy[core_idx] = w
if len(vir_idx) > 0:
w, c1 = mc._eig(fock[vir_idx[:, None], vir_idx], nocc, nmo,
orbsym[vir_idx])
if sort:
idx = numpy.argsort(w.round(9), kind='mergesort')
w = w[idx]
c1 = c1[:, idx]
orbsym[vir_idx] = orbsym[vir_idx][idx]
mo_coeff1[:, vir_idx] = numpy.dot(mo_coeff1[:, vir_idx], c1)
mo_energy[vir_idx] = w
if getattr(mo_coeff, 'orbsym', None) is not None:
mo_coeff1 = lib.tag_array(mo_coeff1, orbsym=orbsym)
if log.verbose >= logger.DEBUG:
for i in range(nmo):
log.debug('i = %d <i|F|i> = %12.8f', i + 1, mo_energy[i])
# still return ci coefficients, in case the canonicalization funciton changed
# cas orbitals, the ci coefficients should also be updated.
return mo_coeff1, ci, mo_energy
def cas_natorb(mc,
mo_coeff=None,
ci=None,
eris=None,
sort=False,
casdm1=None,
verbose=None,
with_meta_lowdin=WITH_META_LOWDIN):
'''Transform active orbitals to natrual orbitals, and update the CI wfn
accordingly
Args:
mc : a CASSCF/CASCI object or RHF object
Kwargs:
sort : bool
Sort natural orbitals wrt the occupancy.
Returns:
A tuple, the first item is natural orbitals, the second is updated CI
coefficients, the third is the natural occupancy associated to the
natural orbitals.
'''
from pyscf.lo import orth
from pyscf.tools import dump_mat
from pyscf.tools.mo_mapping import mo_1to1map
if mo_coeff is None: mo_coeff = mc.mo_coeff
if ci is None: ci = mc.ci
log = logger.new_logger(mc, verbose)
ncore = mc.ncore
ncas = mc.ncas
nocc = ncore + ncas
nelecas = mc.nelecas
if casdm1 is None:
casdm1 = mc.fcisolver.make_rdm1(ci, ncas, nelecas)
# orbital symmetry is reserved in this _eig call
occ, ucas = mc._eig(-casdm1, ncore, nocc)
if sort:
casorb_idx = numpy.argsort(occ.round(9), kind='mergesort')
occ = occ[casorb_idx]
ucas = ucas[:, casorb_idx]
occ = -occ
mo_occ = numpy.zeros(mo_coeff.shape[1])
mo_occ[:ncore] = 2
mo_occ[ncore:nocc] = occ
mo_coeff1 = mo_coeff.copy()
mo_coeff1[:, ncore:nocc] = numpy.dot(mo_coeff[:, ncore:nocc], ucas)
if getattr(mo_coeff, 'orbsym', None) is not None:
orbsym = numpy.copy(mo_coeff.orbsym)
if sort:
orbsym[ncore:nocc] = orbsym[ncore:nocc][casorb_idx]
mo_coeff1 = lib.tag_array(mo_coeff1, orbsym=orbsym)
fcivec = None
if getattr(mc.fcisolver, 'transform_ci_for_orbital_rotation', None):
if isinstance(ci, numpy.ndarray):
fcivec = mc.fcisolver.transform_ci_for_orbital_rotation(
ci, ncas, nelecas, ucas)
elif (isinstance(ci, (tuple, list))
and all(isinstance(x[0], numpy.ndarray) for x in ci)):
fcivec = [
mc.fcisolver.transform_ci_for_orbital_rotation(
x, ncas, nelecas, ucas) for x in ci
]
if fcivec is None:
log.info('FCI vector not available, call CASCI to update wavefunction')
mocas = mo_coeff1[:, ncore:nocc]
hcore = mc.get_hcore()
dm_core = numpy.dot(mo_coeff1[:, :ncore] * 2, mo_coeff1[:, :ncore].T)
ecore = mc.energy_nuc()
ecore += numpy.einsum('ij,ji', hcore, dm_core)
h1eff = reduce(numpy.dot, (mocas.T, hcore, mocas))
if getattr(eris, 'ppaa', None) is not None:
ecore += eris.vhf_c[:ncore, :ncore].trace()
h1eff += reduce(numpy.dot,
(ucas.T, eris.vhf_c[ncore:nocc, ncore:nocc], ucas))
aaaa = ao2mo.restore(4, eris.ppaa[ncore:nocc, ncore:nocc, :, :],
ncas)
aaaa = ao2mo.incore.full(aaaa, ucas)
else:
if getattr(mc, 'with_df', None):
raise NotImplementedError('cas_natorb for DFCASCI/DFCASSCF')
corevhf = mc.get_veff(mc.mol, dm_core)
ecore += numpy.einsum('ij,ji', dm_core, corevhf) * .5
h1eff += reduce(numpy.dot, (mocas.T, corevhf, mocas))
aaaa = ao2mo.kernel(mc.mol, mocas)
# See label_symmetry_ function in casci_symm.py which initialize the
# orbital symmetry information in fcisolver. This orbital symmetry
# labels should be reordered to match the sorted active space orbitals.
if sort and getattr(mo_coeff1, 'orbsym', None) is not None:
mc.fcisolver.orbsym = mo_coeff1.orbsym[ncore:nocc]
max_memory = max(400, mc.max_memory - lib.current_memory()[0])
e, fcivec = mc.fcisolver.kernel(h1eff,
aaaa,
ncas,
nelecas,
ecore=ecore,
max_memory=max_memory,
verbose=log)
log.debug('In Natural orbital, CASCI energy = %s', e)
if log.verbose >= logger.INFO:
ovlp_ao = mc._scf.get_ovlp()
# where_natorb gives the new locations of the natural orbitals
where_natorb = mo_1to1map(ucas)
log.debug('where_natorb %s', str(where_natorb))
log.info('Natural occ %s', str(occ))
if with_meta_lowdin:
log.info(
'Natural orbital (expansion on meta-Lowdin AOs) in CAS space')
label = mc.mol.ao_labels()
orth_coeff = orth.orth_ao(mc.mol, 'meta_lowdin', s=ovlp_ao)
mo_cas = reduce(numpy.dot,
(orth_coeff.T, ovlp_ao, mo_coeff1[:, ncore:nocc]))
else:
log.info('Natural orbital (expansion on AOs) in CAS space')
label = mc.mol.ao_labels()
mo_cas = mo_coeff1[:, ncore:nocc]
dump_mat.dump_rec(log.stdout, mo_cas, label, start=1)
if mc._scf.mo_coeff is not None:
s = reduce(numpy.dot, (mo_coeff1[:, ncore:nocc].T,
mc._scf.get_ovlp(), mc._scf.mo_coeff))
idx = numpy.argwhere(abs(s) > .4)
for i, j in idx:
log.info('<CAS-nat-orb|mo-hf> %d %d %12.8f', ncore + i + 1,
j + 1, s[i, j])
return mo_coeff1, fcivec, mo_occ
def get_veff(ks, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
'''Coulomb + XC functional
.. note::
This function will change the ks object.
Args:
ks : an instance of :class:`RKS`
XC functional are controlled by ks.xc attribute. Attribute
ks.grids might be initialized.
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Kwargs:
dm_last : ndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the
increment of HF potential w.r.t. the reference HF potential matrix.
vhf_last : ndarray or a list of ndarrays or 0
The reference Vxc potential matrix.
hermi : int
Whether J, K matrix is hermitian
| 0 : no hermitian or symmetric
| 1 : hermitian
| 2 : anti-hermitian
Returns:
matrix Veff = J + Vxc. Veff can be a list matrices, if the input
dm is a list of density matrices.
'''
if mol is None: mol = ks.mol
if dm is None: dm = ks.make_rdm1()
t0 = (time.clock(), time.time())
ground_state = (isinstance(dm, numpy.ndarray) and dm.ndim == 2)
if ks.grids.coords is None:
ks.grids.build(with_non0tab=True)
if ks.small_rho_cutoff > 1e-20 and ground_state:
ks.grids = rks.prune_small_rho_grids_(ks, mol, dm, ks.grids)
t0 = logger.timer(ks, 'setting up grids', *t0)
if hermi == 2: # because rho = 0
n, exc, vxc = 0, 0, 0
else:
max_memory = ks.max_memory - lib.current_memory()[0]
n, exc, vxc = ks._numint.r_vxc(mol, ks.grids, ks.xc, dm, hermi=hermi,
max_memory=max_memory)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
omega, alpha, hyb = ks._numint.rsh_and_hybrid_coeff(ks.xc, spin=mol.spin)
if abs(hyb) < 1e-10:
vk = None
if (ks._eri is None and ks.direct_scf and
getattr(vhf_last, 'vj', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj = ks.get_j(mol, ddm, hermi)
vj += vhf_last.vj
else:
vj = ks.get_j(mol, dm, hermi)
vxc += vj
else:
if (ks._eri is None and ks.direct_scf and
getattr(vhf_last, 'vk', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj, vk = ks.get_jk(mol, ddm, hermi)
vk *= hyb
if abs(omega) > 1e-10: # For range separated Coulomb operator
vklr = ks.get_k(mol, ddm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vj += vhf_last.vj
vk += vhf_last.vk
else:
vj, vk = ks.get_jk(mol, dm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, dm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vxc += vj - vk
if ground_state:
exc -= numpy.einsum('ij,ji', dm, vk).real * hyb * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm, vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=vj, vk=vk)
return vxc
def get_veff(ks,
cell=None,
dm=None,
dm_last=0,
vhf_last=0,
hermi=1,
kpts=None,
kpts_band=None):
"""
Coulomb + XC functional + Hubbard U terms.
.. note::
This is a replica of pyscf.dft.rks.get_veff with kpts added.
This function will change the ks object.
Args:
ks : an instance of :class:`RKS`
XC functional are controlled by ks.xc attribute. Attribute
ks.grids might be initialized.
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Returns:
Veff : (nkpts, nao, nao) or (*, nkpts, nao, nao) ndarray
Veff = J + Vxc + V_U.
"""
if cell is None: cell = ks.cell
if dm is None: dm = ks.make_rdm1()
if kpts is None: kpts = ks.kpts
# J + V_xc
vxc = krks.get_veff(ks,
cell=cell,
dm=dm,
dm_last=dm_last,
vhf_last=vhf_last,
hermi=hermi,
kpts=kpts,
kpts_band=kpts_band)
# V_U
C_ao_lo = ks.C_ao_lo
ovlp = ks.get_ovlp()
nkpts = len(kpts)
nlo = C_ao_lo.shape[-1]
rdm1_lo = np.zeros((nkpts, nlo, nlo), dtype=np.complex128)
for k in range(nkpts):
C_inv = np.dot(C_ao_lo[k].conj().T, ovlp[k])
rdm1_lo[k] = mdot(C_inv, dm[k], C_inv.conj().T)
E_U = 0.0
weight = 1.0 / nkpts
logger.info(ks, "-" * 79)
with np.printoptions(precision=5, suppress=True, linewidth=1000):
for idx, val, lab in zip(ks.U_idx, ks.U_val, ks.U_lab):
lab_string = " "
for l in lab:
lab_string += "%9s" % (l.split()[-1])
lab_sp = lab[0].split()
logger.info(ks, "local rdm1 of atom %s: ",
" ".join(lab_sp[:2]) + " " + lab_sp[2][:2])
U_mesh = np.ix_(idx, idx)
P_loc = 0.0
for k in range(nkpts):
S_k = ovlp[k]
C_k = C_ao_lo[k][:, idx]
P_k = rdm1_lo[k][U_mesh]
SC = np.dot(S_k, C_k)
vxc[k] += mdot(SC, (np.eye(P_k.shape[-1]) - P_k) * (val * 0.5),
SC.conj().T).astype(vxc[k].dtype, copy=False)
E_U += (val * 0.5) * (P_k.trace() -
np.dot(P_k, P_k).trace() * 0.5)
P_loc += P_k
P_loc = P_loc.real / nkpts
logger.info(ks, "%s\n%s", lab_string, P_loc)
logger.info(ks, "-" * 79)
E_U *= weight
if E_U.real < 0.0 and all(np.asarray(ks.U_val) > 0):
logger.warn(ks, "E_U (%s) is negative...", E_U.real)
vxc = lib.tag_array(vxc, E_U=E_U)
return vxc
def get_nto(tdobj, state=1, threshold=OUTPUT_THRESHOLD, verbose=None):
r'''
Natural transition orbital analysis.
The natural transition density matrix between ground state and excited
state :math:`Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle` can
be transformed to diagonal form through SVD
:math:`T = O \sqrt{\lambda} V^\dagger`. O and V are occupied and virtual
natural transition orbitals. The diagonal elements :math:`\lambda` are the
weights of the occupied-virtual orbital pair in the excitation.
Ref: Martin, R. L., JCP, 118, 4775-4777
Note in the TDHF/TDDFT calculations, the excitation part (X) is
interpreted as the CIS coefficients and normalized to 1. The de-excitation
part (Y) is ignored.
Args:
state : int
Excited state ID. state = 1 means the first excited state.
If state < 0, state ID is counted from the last excited state.
Kwargs:
threshold : float
Above which the NTO coefficients will be printed in the output.
Returns:
A list (weights, NTOs). NTOs are natural orbitals represented in AO
basis. The first N_occ NTOs are occupied NTOs and the rest are virtual
NTOs.
'''
if state == 0:
logger.warn(
tdobj, 'Excited state starts from 1. '
'Set state=1 for first excited state.')
state_id = state
elif state < 0:
state_id = state
else:
state_id = state - 1
mol = tdobj.mol
mo_coeff = tdobj._scf.mo_coeff
mo_occ = tdobj._scf.mo_occ
orbo = mo_coeff[:, mo_occ == 2]
orbv = mo_coeff[:, mo_occ == 0]
nocc = orbo.shape[1]
nvir = orbv.shape[1]
cis_t1 = tdobj.xy[state_id][0]
# TDDFT (X,Y) has X^2-Y^2=1.
# Renormalizing X (X^2=1) to map it to CIS coefficients
cis_t1 *= 1. / numpy.linalg.norm(cis_t1)
# TODO: Comparing to the NTOs defined in JCP, 142, 244103. JCP, 142, 244103
# provides a method to incorporate the Y matrix in the transition density
# matrix. However, it may break the point group symmetry of the NTO orbitals
# when the system has degenerated irreducible representations.
if mol.symmetry:
orbsym = hf_symm.get_orbsym(mol, mo_coeff)
o_sym = orbsym[mo_occ == 2]
v_sym = orbsym[mo_occ == 0]
nto_o = numpy.eye(nocc)
nto_v = numpy.eye(nvir)
weights_o = numpy.zeros(nocc)
weights_v = numpy.zeros(nvir)
for ir in set(orbsym):
idx = numpy.where(o_sym == ir)[0]
if idx.size > 0:
dm_oo = numpy.dot(cis_t1[idx], cis_t1[idx].T)
weights_o[idx], nto_o[idx[:, None],
idx] = numpy.linalg.eigh(dm_oo)
idx = numpy.where(v_sym == ir)[0]
if idx.size > 0:
dm_vv = numpy.dot(cis_t1[:, idx].T, cis_t1[:, idx])
weights_v[idx], nto_v[idx[:, None],
idx] = numpy.linalg.eigh(dm_vv)
# weights in descending order
idx = numpy.argsort(-weights_o)
weights_o = weights_o[idx]
nto_o = nto_o[:, idx]
o_sym = o_sym[idx]
idx = numpy.argsort(-weights_v)
weights_v = weights_v[idx]
nto_v = nto_v[:, idx]
v_sym = v_sym[idx]
nto_orbsym = numpy.hstack((o_sym, v_sym))
if nocc < nvir:
weights = weights_o
else:
weights = weights_v
else:
nto_o, w, nto_vT = numpy.linalg.svd(cis_t1)
nto_v = nto_vT.conj().T
weights = w**2
nto_orbsym = None
idx = numpy.argmax(abs(nto_o.real), axis=0)
nto_o[:, nto_o[idx, numpy.arange(nocc)].real < 0] *= -1
idx = numpy.argmax(abs(nto_v.real), axis=0)
nto_v[:, nto_v[idx, numpy.arange(nvir)].real < 0] *= -1
occupied_nto = numpy.dot(orbo, nto_o)
virtual_nto = numpy.dot(orbv, nto_v)
nto_coeff = numpy.hstack((occupied_nto, virtual_nto))
if mol.symmetry:
nto_coeff = lib.tag_array(nto_coeff, orbsym=nto_orbsym)
log = logger.new_logger(tdobj, verbose)
if log.verbose >= logger.INFO:
log.info('State %d: %g eV NTO largest component %s', state_id + 1,
tdobj.e[state_id] * nist.HARTREE2EV, weights[0])
o_idx = numpy.where(abs(nto_o[:, 0]) > threshold)[0]
v_idx = numpy.where(abs(nto_v[:, 0]) > threshold)[0]
fmt = '%' + str(lib.param.OUTPUT_DIGITS) + 'f (MO #%d)'
log.info(' occ-NTO: ' + ' + '.join([(fmt %
(nto_o[i, 0], i + MO_BASE))
for i in o_idx]))
log.info(' vir-NTO: ' +
' + '.join([(fmt % (nto_v[i, 0], i + MO_BASE + nocc))
for i in v_idx]))
return weights, nto_coeff
def get_veff(ks, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
kpts=None, kpts_band=None):
'''Coulomb + XC functional
.. note::
This is a replica of pyscf.dft.rks.get_veff with kpts added.
This function will change the ks object.
Args:
ks : an instance of :class:`RKS`
XC functional are controlled by ks.xc attribute. Attribute
ks.grids might be initialized.
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Returns:
Veff : (nkpts, nao, nao) or (*, nkpts, nao, nao) ndarray
Veff = J + Vxc.
'''
if cell is None: cell = ks.cell
if dm is None: dm = ks.make_rdm1()
if kpts is None: kpts = ks.kpts
t0 = (time.clock(), time.time())
omega, alpha, hyb = ks._numint.rsh_and_hybrid_coeff(ks.xc, spin=cell.spin)
hybrid = abs(hyb) > 1e-10
if not hybrid and isinstance(ks.with_df, multigrid.MultiGridFFTDF):
n, exc, vxc = multigrid.nr_rks(ks.with_df, ks.xc, dm, hermi,
kpts, kpts_band,
with_j=True, return_j=False)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
return vxc
# ndim = 3 : dm.shape = (nkpts, nao, nao)
ground_state = (isinstance(dm, np.ndarray) and dm.ndim == 3 and
kpts_band is None)
# For UniformGrids, grids.coords does not indicate whehter grids are initialized
if ks.grids.non0tab is None:
ks.grids.build(with_non0tab=True)
if (isinstance(ks.grids, gen_grid.BeckeGrids) and
ks.small_rho_cutoff > 1e-20 and ground_state):
ks.grids = rks.prune_small_rho_grids_(ks, cell, dm, ks.grids, kpts)
t0 = logger.timer(ks, 'setting up grids', *t0)
if hermi == 2: # because rho = 0
n, exc, vxc = 0, 0, 0
else:
n, exc, vxc = ks._numint.nr_rks(cell, ks.grids, ks.xc, dm, 0,
kpts, kpts_band)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
weight = 1./len(kpts)
if not hybrid:
vj = ks.get_j(cell, dm, hermi, kpts, kpts_band)
vxc += vj
else:
if getattr(ks.with_df, '_j_only', False): # for GDF and MDF
ks.with_df._j_only = False
vj, vk = ks.get_jk(cell, dm, hermi, kpts, kpts_band)
vxc += vj - vk * (hyb * .5)
if ground_state:
exc -= np.einsum('Kij,Kji', dm, vk).real * .5 * hyb*.5 * weight
if ground_state:
ecoul = np.einsum('Kij,Kji', dm, vj).real * .5 * weight
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=None, vk=None)
return vxc
def rotate_mo(self, mo_coeff, u, log=None):
mo = numpy.dot(mo_coeff, u)
if self._scf.mol.symmetry:
orbsym = hf_symm.get_orbsym(self._scf.mol, mo_coeff)
mo = lib.tag_array(mo, orbsym=orbsym)
return mo
def get_veff(mf, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
t0 = (time.clock(), time.time())
mf.unpack_(comm.bcast(mf.pack()))
mol = mf.mol
ni = mf._numint
if mf.nlc != '':
raise NotImplementedError
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(mf.xc, spin=mol.spin)
if abs(omega) > 1e-10: # For range separated Coulomb operator
raise NotImplementedError
# Broadcast the large input arrays here.
if any(comm.allgather(isinstance(dm, str) and dm == 'SKIPPED_ARG')):
if rank == 0 and dm is None:
dm = mf.make_rdm1()
dm = mpi.bcast_tagged_array(dm)
if any(comm.allgather(isinstance(dm_last, str) and dm_last == 'SKIPPED_ARG')):
dm_last = mpi.bcast_tagged_array(dm_last)
if any(comm.allgather(isinstance(vhf_last, str) and vhf_last == 'SKIPPED_ARG')):
vhf_last = mpi.bcast_tagged_array(vhf_last)
ground_state = (dm.ndim == 3 and dm.shape[0] == 2)
if mf.grids.coords is None:
mpi_rks._setup_grids_(mf, dm[0]+dm[1])
t0 = logger.timer(mf, 'setting up grids', *t0)
if hermi == 2: # because rho = 0
n, exc, vxc = 0, 0, 0
else:
n, exc, vxc = ni.nr_uks(mol, mf.grids, mf.xc, dm)
n = comm.allreduce(n)
exc = comm.allreduce(exc)
vxc = mpi.reduce(vxc)
logger.debug(mf, 'nelec by numeric integration = %s', n)
t0 = logger.timer(mf, 'vxc', *t0)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vk = None
if getattr(vhf_last, 'vj', None) is not None:
ddm = numpy.asarray(dm) - dm_last
ddm = ddm[0] + ddm[1]
vj = mpi.reduce(mf.get_j(mol, ddm, hermi))
vj += vhf_last.vj
else:
vj = mf.get_j(mol, dm[0]+dm[1], hermi)
vj = mpi.reduce(vj)
vxc += vj
else:
if getattr(vhf_last, 'vk', None) is not None:
ddm = numpy.asarray(dm) - dm_last
vj, vk = mf.get_jk(mol, ddm, hermi)
ddm = None
vj = mpi.reduce(vj[0] + vj[1])
vk = mpi.reduce(vk) * hyb
vj += vhf_last.vj
vk += vhf_last.vk
else:
vj, vk = mf.get_jk(mol, dm, hermi)
vj = mpi.reduce(vj[0] + vj[1])
vk = mpi.reduce(vk) * hyb
vxc += vj
vxc -= vk
if ground_state:
exc -=(numpy.einsum('ij,ji', dm[0], vk[0]) +
numpy.einsum('ij,ji', dm[1], vk[1])) * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm[0]+dm[1], vj) * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=vj, vk=vk)
return vxc
def kernel(ot,
oneCDMs_amo,
twoCDM_amo,
mo_coeff,
ncore,
ncas,
max_memory=2000,
hermi=1,
paaa_only=False,
aaaa_only=False):
''' Get the 1- and 2-body effective potential from MC-PDFT.
Args:
ot : an instance of otfnal class
oneCDMs_amo : ndarray of shape (2, ncas, ncas)
containing spin-separated one-body density matrices
twoCDM_amo : ndarray of shape (ncas, ncas, ncas, ncas)
containing spin-summed two-body cumulant density matrix in an
active space
mo_coeff : ndarray of shape (nao, nmo)
containing molecular orbital coefficients
ncore : integer
number of inactive orbitals
ncas : integer
number of active orbitals
Kwargs:
max_memory : int or float
maximum cache size in MB
default is 2000
hermi : int
1 if 1CDMs are assumed hermitian, 0 otherwise
paaa_only : logical
If true, only compute the paaa range of papa and ppaa (all
other elements set to zero)
aaaa_only : logical
If true, only compute the aaaa range of papa and ppaa (all
other elements set to zero; overrides paaa_only)
Returns:
veff1 : ndarray of shape (nao, nao)
1-body effective potential
veff2 : object of class pdft_veff._ERIS
2-body effective potential and related quantities
'''
nocc = ncore + ncas
ni, xctype, dens_deriv = ot._numint, ot.xctype, ot.dens_deriv
nao = mo_coeff.shape[0]
mo_core = mo_coeff[:, :ncore]
ao2amo = mo_coeff[:, ncore:nocc]
npair = nao * (nao + 1) // 2
shls_slice = (0, ot.mol.nbas)
ao_loc = ot.mol.ao_loc_nr()
veff1 = np.zeros((nao, nao), dtype=oneCDMs_amo.dtype)
veff2 = _ERIS(ot.mol,
mo_coeff,
ncore,
ncas,
paaa_only=paaa_only,
aaaa_only=aaaa_only,
verbose=ot.verbose,
stdout=ot.stdout)
t0 = (time.process_time(), time.time())
# Make density matrices and TAG THEM with their own eigendecompositions
# because that speeds up the rho generators!
dm_core = mo_core @ mo_core.T
dm_cas = np.dot(ao2amo, np.dot(oneCDMs_amo, ao2amo.T)).transpose(1, 0, 2)
dm1s = dm_cas + dm_core[None, :, :]
dm_core *= 2
# tag dm_core
imo_occ = np.ones(ncore, dtype=dm_core.dtype) * 2.0
dm_core = tag_array(dm_core, mo_coeff=mo_core, mo_occ=imo_occ)
# tag dm_cas
amo_occ = np.zeros((2, ncas), dtype=dm_cas.dtype)
amo_coeff = np.stack([ao2amo.copy(), ao2amo.copy()], axis=0)
for i in range(2):
amo_occ[i], ua = linalg.eigh(oneCDMs_amo[i])
amo_coeff[i] = amo_coeff[i] @ ua
dm_cas = tag_array(dm_cas, mo_coeff=amo_coeff, mo_occ=amo_occ)
# tag dm1s
mo_occ = np.zeros((2, nocc), dtype=dm1s.dtype)
mo_occ[:, :ncore] = 1.0
mo_occ[:, ncore:nocc] = amo_occ
tag_coeff = np.stack(
(mo_coeff[:, :nocc].copy(), mo_coeff[:, :nocc].copy()), axis=0)
tag_coeff[:, :, ncore:nocc] = amo_coeff
dm1s = tag_array(dm1s, mo_coeff=tag_coeff, mo_occ=mo_occ)
# rho generators
make_rho_c, nset_c, nao_c = ni._gen_rho_evaluator(ot.mol, dm_core, hermi)
make_rho_a, nset_a, nao_a = ni._gen_rho_evaluator(ot.mol, dm_cas, hermi)
make_rho, nset, nao_ = ni._gen_rho_evaluator(ot.mol, dm1s, hermi)
# memory block size
gc.collect()
remaining_floats = (max_memory - current_memory()[0]) * 1e6 / 8
nderiv_rho = (1, 4, 10)[dens_deriv] # ?? for meta-GGA
nderiv_Pi = (1, 4)[ot.Pi_deriv]
ncols = 4 + nderiv_rho * nao # ao, weight, coords
ncols += nderiv_rho * 4 + nderiv_Pi # rho, rho_a, rho_c, Pi
ncols += 1 + nderiv_rho + nderiv_Pi # eot, vot
# Asynchronous part
nveff1 = nderiv_rho * (nao + 1) # footprint of get_veff_1body
nveff2 = veff2._accumulate_ftpt() * nderiv_Pi
ncols += np.amax([nveff1, nveff2]) # asynchronous fns
pdft_blksize = int(remaining_floats /
(ncols * BLKSIZE)) * BLKSIZE # round up
if ot.grids.coords is None:
ot.grids.build(with_non0tab=True)
ngrids = ot.grids.coords.shape[0]
pdft_blksize = max(BLKSIZE, min(pdft_blksize, ngrids, BLKSIZE * 1200))
logger.debug(
ot,
'{} MB used of {} available; block size of {} chosen for grid with {} points'
.format(current_memory()[0], max_memory, pdft_blksize, ngrids))
# The actual loop
for ao, mask, weight, coords in ni.block_loop(ot.mol,
ot.grids,
nao,
dens_deriv,
max_memory,
blksize=pdft_blksize):
rho = np.asarray([make_rho(i, ao, mask, xctype) for i in range(2)])
rho_a = sum([make_rho_a(i, ao, mask, xctype) for i in range(2)])
rho_c = make_rho_c(0, ao, mask, xctype)
t0 = logger.timer(ot, 'untransformed densities (core and total)', *t0)
Pi = get_ontop_pair_density(ot, rho, ao, dm1s, twoCDM_amo, ao2amo,
dens_deriv, mask)
t0 = logger.timer(ot, 'on-top pair density calculation', *t0)
eot, vot = ot.eval_ot(rho, Pi, weights=weight)[:2]
vrho, vPi = vot
t0 = logger.timer(ot, 'effective potential kernel calculation', *t0)
if ao.ndim == 2:
ao = ao[None, :, :] # TODO: consistent format req's ao LDA case
veff1 += ot.get_veff_1body(rho,
Pi,
ao,
weight,
non0tab=mask,
shls_slice=shls_slice,
ao_loc=ao_loc,
hermi=1,
kern=vrho)
t0 = logger.timer(ot, '1-body effective potential calculation', *t0)
veff2._accumulate(ot, rho, Pi, ao, weight, rho_c, rho_a, vPi, mask,
shls_slice, ao_loc)
t0 = logger.timer(ot, '2-body effective potential calculation', *t0)
veff2._finalize()
t0 = logger.timer(ot, 'Finalizing 2-body effective potential calculation',
*t0)
return veff1, veff2
def get_veff(ks, cell=None, dm=None, dm_last=0, vhf_last=0, hermi=1,
kpt=None, kpts_band=None):
'''Coulomb + XC functional
.. note::
This function will change the ks object.
Args:
ks : an instance of :class:`RKS`
XC functional are controlled by ks.xc attribute. Attribute
ks.grids might be initialized.
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Returns:
matrix Veff = J + Vxc. Veff can be a list matrices, if the input
dm is a list of density matrices.
'''
if cell is None: cell = ks.cell
if dm is None: dm = ks.make_rdm1()
if kpt is None: kpt = ks.kpt
t0 = (time.clock(), time.time())
omega, alpha, hyb = ks._numint.rsh_and_hybrid_coeff(ks.xc, spin=cell.spin)
hybrid = abs(hyb) > 1e-10
if not hybrid and isinstance(ks.with_df, multigrid.MultiGridFFTDF):
n, exc, vxc = multigrid.nr_rks(ks.with_df, ks.xc, dm, hermi,
kpt.reshape(1,3), kpts_band,
with_j=True, return_j=False)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
return vxc
ground_state = (isinstance(dm, numpy.ndarray) and dm.ndim == 2
and kpts_band is None)
# Use grids.non0tab to detect whether grids are initialized. For
# UniformGrids, grids.coords as a property cannot indicate whehter grids are
# initialized.
if ks.grids.non0tab is None:
ks.grids.build(with_non0tab=True)
if (isinstance(ks.grids, gen_grid.BeckeGrids) and
ks.small_rho_cutoff > 1e-20 and ground_state):
ks.grids = prune_small_rho_grids_(ks, cell, dm, ks.grids, kpt)
t0 = logger.timer(ks, 'setting up grids', *t0)
if hermi == 2: # because rho = 0
n, exc, vxc = 0, 0, 0
else:
n, exc, vxc = ks._numint.nr_rks(cell, ks.grids, ks.xc, dm, 0,
kpt, kpts_band)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
if not hybrid:
vj = ks.get_j(cell, dm, hermi, kpt, kpts_band)
vxc += vj
else:
if getattr(ks.with_df, '_j_only', False): # for GDF and MDF
ks.with_df._j_only = False
vj, vk = ks.get_jk(cell, dm, hermi, kpt, kpts_band)
vxc += vj - vk * (hyb * .5)
if ground_state:
exc -= numpy.einsum('ij,ji', dm, vk).real * .5 * hyb*.5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm, vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=None, vk=None)
return vxc
def get_veff(ks, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
'''Coulomb + XC functional
.. note::
This function will change the ks object.
Args:
ks : an instance of :class:`RKS`
XC functional are controlled by ks.xc attribute. Attribute
ks.grids might be initialized.
dm : ndarray or list of ndarrays
A density matrix or a list of density matrices
Kwargs:
dm_last : ndarray or a list of ndarrays or 0
The density matrix baseline. If not 0, this function computes the
increment of HF potential w.r.t. the reference HF potential matrix.
vhf_last : ndarray or a list of ndarrays or 0
The reference Vxc potential matrix.
hermi : int
Whether J, K matrix is hermitian
| 0 : no hermitian or symmetric
| 1 : hermitian
| 2 : anti-hermitian
Returns:
matrix Veff = J + Vxc. Veff can be a list matrices, if the input
dm is a list of density matrices.
'''
if mol is None: mol = ks.mol
if dm is None: dm = ks.make_rdm1()
ks.initialize_grids(mol, dm)
t0 = (logger.process_clock(), logger.perf_counter())
ground_state = isinstance(dm, numpy.ndarray) and dm.ndim == 2
if hermi == 2: # because rho = 0
n, exc, vxc = 0, 0, 0
else:
max_memory = ks.max_memory - lib.current_memory()[0]
ni = ks._numint
n, exc, vxc = ni.get_vxc(mol,
ks.grids,
ks.xc,
dm,
hermi=hermi,
max_memory=max_memory)
if ks.nlc != '':
assert ('VV10' in ks.nlc.upper())
_, enlc, vnlc = ni.get_vxc(mol,
ks.nlcgrids,
ks.xc + '__' + ks.nlc,
dm,
hermi=hermi,
max_memory=max_memory)
exc += enlc
vxc += vnlc
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(ks.xc, spin=mol.spin)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vk = None
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vj', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj = ks.get_j(mol, ddm, hermi)
vj += vhf_last.vj
else:
vj = ks.get_j(mol, dm, hermi)
vxc += vj
else:
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vk', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj, vk = ks.get_jk(mol, ddm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, ddm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vj += vhf_last.vj
vk += vhf_last.vk
else:
vj, vk = ks.get_jk(mol, dm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, dm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vxc += vj - vk
if ground_state:
exc -= numpy.einsum('ij,ji', dm, vk).real * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm, vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=vj, vk=vk)
return vxc
def cas_natorb(mc, mo_coeff=None, ci=None, eris=None, sort=False,
casdm1=None, verbose=None):
'''Transform active orbitals to natrual orbitals, and update the CI wfn
Args:
mc : a CASSCF/CASCI object or RHF object
Kwargs:
sort : bool
Sort natural orbitals wrt the occupancy.
Returns:
A tuple, the first item is natural orbitals, the second is updated CI
coefficients, the third is the natural occupancy associated to the
natural orbitals.
'''
from pyscf.lo import orth
from pyscf.tools import dump_mat
from pyscf.tools.mo_mapping import mo_1to1map
if isinstance(verbose, logger.Logger):
log = verbose
else:
log = logger.Logger(mc.stdout, mc.verbose)
if mo_coeff is None: mo_coeff = mc.mo_coeff
if ci is None: ci = mc.ci
ncore = mc.ncore
ncas = mc.ncas
nocc = ncore + ncas
nelecas = mc.nelecas
if casdm1 is None:
casdm1 = mc.fcisolver.make_rdm1(ci, ncas, nelecas)
# orbital symmetry is reserved in this _eig call
occ, ucas = mc._eig(-casdm1, ncore, nocc)
if sort:
casorb_idx = numpy.argsort(occ.round(9), kind='mergesort')
occ = occ[casorb_idx]
ucas = ucas[:,casorb_idx]
# restore phase
# where_natorb gives the location of the natural orbital for the input cas
# orbitals. gen_strings4orblist map thes sorted strings (on CAS orbital) to
# the unsorted determinant strings (on natural orbital). e.g. (3o,2e) system
# CAS orbital 1 2 3
# natural orbital 3 1 2 <= by mo_1to1map
# CASorb-strings 0b011, 0b101, 0b110
# == (1,2), (1,3), (2,3)
# natorb-strings (3,1), (3,2), (1,2)
# == 0B101, 0B110, 0B011 <= by gen_strings4orblist
# then argsort to translate the string representation to the address
# [2(=0B011), 0(=0B101), 1(=0B110)]
# to indicate which CASorb-strings address to be loaded in each natorb-strings slot
where_natorb = mo_1to1map(ucas)
for i, k in enumerate(where_natorb):
if ucas[i,k] < 0:
ucas[:,k] *= -1
occ = -occ
mo_occ = numpy.zeros(mo_coeff.shape[1])
mo_occ[:ncore] = 2
mo_occ[ncore:nocc] = occ
mo_coeff1 = mo_coeff.copy()
mo_coeff1[:,ncore:nocc] = numpy.dot(mo_coeff[:,ncore:nocc], ucas)
if hasattr(mo_coeff, 'orbsym'):
orbsym = numpy.copy(mo_coeff.orbsym)
if sort:
orbsym[ncore:nocc] = orbsym[ncore:nocc][casorb_idx]
mo_coeff1 = lib.tag_array(mo_coeff1, orbsym=orbsym)
if isinstance(ci, numpy.ndarray):
fcivec = fci.addons.transform_ci_for_orbital_rotation(ci, ncas, nelecas, ucas)
elif isinstance(ci, (tuple, list)) and isinstance(ci[0], numpy.ndarray):
# for state-average eigenfunctions
fcivec = [fci.addons.transform_ci_for_orbital_rotation(x, ncas, nelecas, ucas)
for x in ci]
else:
log.info('FCI vector not available, call CASCI for wavefunction')
mocas = mo_coeff1[:,ncore:nocc]
hcore = mc.get_hcore()
dm_core = numpy.dot(mo_coeff1[:,:ncore]*2, mo_coeff1[:,:ncore].T)
ecore = mc._scf.energy_nuc()
ecore+= numpy.einsum('ij,ji', hcore, dm_core)
h1eff = reduce(numpy.dot, (mocas.T, hcore, mocas))
if eris is not None and hasattr(eris, 'ppaa'):
ecore += eris.vhf_c[:ncore,:ncore].trace()
h1eff += reduce(numpy.dot, (ucas.T, eris.vhf_c[ncore:nocc,ncore:nocc], ucas))
aaaa = ao2mo.restore(4, eris.ppaa[ncore:nocc,ncore:nocc,:,:], ncas)
aaaa = ao2mo.incore.full(aaaa, ucas)
else:
corevhf = mc.get_veff(mc.mol, dm_core)
ecore += numpy.einsum('ij,ji', dm_core, corevhf) * .5
h1eff += reduce(numpy.dot, (mocas.T, corevhf, mocas))
aaaa = ao2mo.kernel(mc.mol, mocas)
# See label_symmetry_ function in casci_symm.py which initialize the
# orbital symmetry information in fcisolver. This orbital symmetry
# labels should be reordered to match the sorted active space orbitals.
if hasattr(mo_coeff1, 'orbsym') and sort:
mc.fcisolver.orbsym = mo_coeff1.orbsym[ncore:nocc]
max_memory = max(400, mc.max_memory-lib.current_memory()[0])
e, fcivec = mc.fcisolver.kernel(h1eff, aaaa, ncas, nelecas, ecore=ecore,
max_memory=max_memory, verbose=log)
log.debug('In Natural orbital, CASCI energy = %.12g', e)
if log.verbose >= logger.INFO:
ovlp_ao = mc._scf.get_ovlp()
log.debug('where_natorb %s', str(where_natorb))
log.info('Natural occ %s', str(occ))
log.info('Natural orbital (expansion on meta-Lowdin AOs) in CAS space')
label = mc.mol.ao_labels()
orth_coeff = orth.orth_ao(mc.mol, 'meta_lowdin', s=ovlp_ao)
mo_cas = reduce(numpy.dot, (orth_coeff.T, ovlp_ao, mo_coeff1[:,ncore:nocc]))
dump_mat.dump_rec(log.stdout, mo_cas, label, start=1)
if mc._scf.mo_coeff is not None:
s = reduce(numpy.dot, (mo_coeff1[:,ncore:nocc].T,
mc._scf.get_ovlp(), mc._scf.mo_coeff))
idx = numpy.argwhere(abs(s)>.4)
for i,j in idx:
log.info('<CAS-nat-orb|mo-hf> %d %d %12.8f',
ncore+i+1, j+1, s[i,j])
return mo_coeff1, fcivec, mo_occ
def sort_mo(casscf, mo_coeff, caslst, base=BASE):
'''Pick orbitals for CAS space
Args:
casscf : an :class:`CASSCF` or :class:`CASCI` object
mo_coeff : ndarray or a list of ndarray
Orbitals for CASSCF initial guess. In the UHF-CASSCF, it's a list
of two orbitals, for alpha and beta spin.
caslst : list of int or nested list of int
A list of orbital indices to represent the CAS space. In the UHF-CASSCF,
it's consist of two lists, for alpha and beta spin.
Kwargs:
base : int
0-based (C-style) or 1-based (Fortran-style) caslst
Returns:
An reoreded mo_coeff, which put the orbitals given by caslst in the CAS space
Examples:
>>> from pyscf import gto, scf, mcscf
>>> mol = gto.M(atom='N 0 0 0; N 0 0 1', basis='ccpvdz', verbose=0)
>>> mf = scf.RHF(mol)
>>> mf.scf()
>>> mc = mcscf.CASSCF(mf, 4, 4)
>>> cas_list = [5,6,8,9] # pi orbitals
>>> mo = mc.sort_mo(cas_list)
>>> mc.kernel(mo)[0]
-109.007378939813691
'''
ncore = casscf.ncore
def ext_list(nmo, caslst):
mask = numpy.ones(nmo, dtype=bool)
mask[caslst] = False
idx = numpy.where(mask)[0]
if len(idx) + casscf.ncas != nmo:
raise ValueError('Active space size is incompatible with caslist. '
'ncas = %d. caslist %s' % (casscf.ncas, caslst))
return idx
if isinstance(ncore, (int, numpy.integer)):
nmo = mo_coeff.shape[1]
if base != 0:
caslst = [i - base for i in caslst]
idx = ext_list(nmo, caslst)
mo = numpy.hstack((mo_coeff[:, idx[:ncore]], mo_coeff[:, caslst],
mo_coeff[:, idx[ncore:]]))
if getattr(mo_coeff, 'orbsym', None) is not None:
orbsym = mo_coeff.orbsym
orbsym = numpy.hstack(
(orbsym[idx[:ncore]], orbsym[caslst], orbsym[idx[ncore:]]))
mo = lib.tag_array(mo, orbsym=orbsym)
return mo
else: # UHF-based CASSCF
if isinstance(caslst[0], (int, numpy.integer)):
if base != 0:
caslsta = [i - 1 for i in caslst]
caslst = (caslsta, caslsta)
else: # two casspace lists, for alpha and beta
if base != 0:
caslst = ([i - base
for i in caslst[0]], [i - base for i in caslst[1]])
nmo = mo_coeff[0].shape[1]
idxa = ext_list(nmo, caslst[0])
mo_a = numpy.hstack(
(mo_coeff[0][:, idxa[:ncore[0]]], mo_coeff[0][:, caslst[0]],
mo_coeff[0][:, idxa[ncore[0]:]]))
idxb = ext_list(nmo, caslst[1])
mo_b = numpy.hstack(
(mo_coeff[1][:, idxb[:ncore[1]]], mo_coeff[1][:, caslst[1]],
mo_coeff[1][:, idxb[ncore[1]:]]))
if getattr(mo_coeff[0], 'orbsym', None) is not None:
orbsyma, orbsymb = mo_coeff[0].orbsym, mo_coeff[1].orbsym
orbsyma = numpy.hstack(
(orbsyma[idxa[:ncore[0]]], orbsyma[caslst[0]],
orbsyma[idxa[ncore[0]:]]))
orbsymb = numpy.hstack(
(orbsymb[idxb[:ncore[1]]], orbsymb[caslst[1]],
orbsymb[idxb[ncore[1]:]]))
mo_a = lib.tag_array(mo_a, orbsym=orbsyma)
mo_b = lib.tag_array(mo_b, orbsym=orbsymb)
return (mo_a, mo_b)
def _make_eris_incore(cc, mo_coeff=None):
log = logger.Logger(cc.stdout, cc.verbose)
cput0 = (time.clock(), time.time())
eris = gccsd._PhysicistsERIs()
kpts = cc.kpts
nkpts = cc.nkpts
nocc = cc.nocc
nmo = cc.nmo
nvir = nmo - nocc
eris.nocc = nocc
#if any(nocc != numpy.count_nonzero(cc._scf.mo_occ[k] > 0) for k in range(nkpts)):
# raise NotImplementedError('Different occupancies found for different k-points')
if mo_coeff is None:
mo_coeff = cc.mo_coeff
#else:
# # If mo_coeff is not canonical orbital
# # TODO does this work for k-points? changed to conjugate.
# raise NotImplementedError
nao = mo_coeff[0].shape[0]
dtype = mo_coeff[0].dtype
moidx = get_frozen_mask(cc)
nocc_per_kpt = numpy.asarray(get_nocc(cc, per_kpoint=True))
nmo_per_kpt = numpy.asarray(get_nmo(cc, per_kpoint=True))
padded_moidx = []
for k in range(nkpts):
kpt_nocc = nocc_per_kpt[k]
kpt_nvir = nmo_per_kpt[k] - kpt_nocc
kpt_padded_moidx = numpy.concatenate(
(numpy.ones(kpt_nocc, dtype=numpy.bool),
numpy.zeros(nmo - kpt_nocc - kpt_nvir, dtype=numpy.bool),
numpy.ones(kpt_nvir, dtype=numpy.bool)))
padded_moidx.append(kpt_padded_moidx)
eris.mo_coeff = []
eris.orbspin = []
# Generate the molecular orbital coefficients with the frozen orbitals masked.
# Each MO is tagged with orbspin, a list of 0's and 1's that give the overall
# spin of each MO.
#
# Here we will work with two index arrays; one is for our original (small) moidx
# array while the next is for our new (large) padded array.
for k in range(nkpts):
kpt_moidx = moidx[k]
kpt_padded_moidx = padded_moidx[k]
mo = numpy.zeros((nao, nmo), dtype=dtype)
mo[:, kpt_padded_moidx] = mo_coeff[k][:, kpt_moidx]
if hasattr(mo_coeff[k], 'orbspin'):
orbspin_dtype = mo_coeff[k].orbspin[kpt_moidx].dtype
orbspin = numpy.zeros(nmo, dtype=orbspin_dtype)
orbspin[kpt_padded_moidx] = mo_coeff[k].orbspin[kpt_moidx]
mo = lib.tag_array(mo, orbspin=orbspin)
eris.orbspin.append(orbspin)
# FIXME: What if the user freezes all up spin orbitals in
# an RHF calculation? The number of electrons will still be
# even.
else: # guess orbital spin - assumes an RHF calculation
assert (numpy.count_nonzero(kpt_moidx) % 2 == 0)
orbspin = numpy.zeros(mo.shape[1], dtype=int)
orbspin[1::2] = 1
mo = lib.tag_array(mo, orbspin=orbspin)
eris.orbspin.append(orbspin)
eris.mo_coeff.append(mo)
# Re-make our fock MO matrix elements from density and fock AO
dm = cc._scf.make_rdm1(cc.mo_coeff, cc.mo_occ)
fockao = cc._scf.get_hcore() + cc._scf.get_veff(cc._scf.cell, dm)
eris.fock = numpy.asarray([
reduce(numpy.dot, (mo.T.conj(), fockao[k], mo))
for k, mo in enumerate(eris.mo_coeff)
])
kconserv = kpts_helper.get_kconserv(cc._scf.cell, cc.kpts)
# The bottom nao//2 coefficients are down (up) spin while the top are up (down).
# These are 'spin-less' quantities; spin-conservation will be added manually.
so_coeff = [mo[:nao // 2] + mo[nao // 2:] for mo in eris.mo_coeff]
eri = numpy.empty((nkpts, nkpts, nkpts, nmo, nmo, nmo, nmo),
dtype=numpy.complex128)
fao2mo = cc._scf.with_df.ao2mo
for kp, kq, kr in kpts_helper.loop_kkk(nkpts):
ks = kconserv[kp, kq, kr]
eri_kpt = fao2mo(
(so_coeff[kp], so_coeff[kq], so_coeff[kr], so_coeff[ks]),
(kpts[kp], kpts[kq], kpts[kr], kpts[ks]),
compact=False)
eri_kpt[(eris.orbspin[kp][:, None] != eris.orbspin[kq]).ravel()] = 0
eri_kpt[:, (eris.orbspin[kr][:, None] != eris.orbspin[ks]).ravel()] = 0
eri_kpt = eri_kpt.reshape(nmo, nmo, nmo, nmo)
eri[kp, kq, kr] = eri_kpt
# Check some antisymmetrized properties of the integrals
if DEBUG:
check_antisymm_3412(cc, cc.kpts, eri)
# Antisymmetrizing (pq|rs)-(ps|rq), where the latter integral is equal to
# (rq|ps); done since we aren't tracking the kpoint of orbital 's'
eri = eri - eri.transpose(2, 1, 0, 5, 4, 3, 6)
# Chemist -> physics notation
eri = eri.transpose(0, 2, 1, 3, 5, 4, 6)
# Set the various integrals
eris.dtype = eri.dtype
eris.oooo = eri[:, :, :, :nocc, :nocc, :nocc, :nocc].copy() / nkpts
eris.ooov = eri[:, :, :, :nocc, :nocc, :nocc, nocc:].copy() / nkpts
eris.ovoo = eri[:, :, :, :nocc, nocc:, :nocc, :nocc].copy() / nkpts
eris.oovv = eri[:, :, :, :nocc, :nocc, nocc:, nocc:].copy() / nkpts
eris.ovov = eri[:, :, :, :nocc, nocc:, :nocc, nocc:].copy() / nkpts
eris.ovvv = eri[:, :, :, :nocc, nocc:, nocc:, nocc:].copy() / nkpts
eris.vvvv = eri[:, :, :, nocc:, nocc:, nocc:, nocc:].copy() / nkpts
log.timer('CCSD integral transformation', *cput0)
return eris
def canonicalize(mc, mo_coeff=None, ci=None, eris=None, sort=False,
cas_natorb=False, casdm1=None, verbose=logger.NOTE):
'''Canonicalized CASCI/CASSCF orbitals of effecitve Fock matrix.
Effective Fock matrix is built with one-particle density matrix (see
also :func:`mcscf.casci.get_fock`)
Args:
mc : a CASSCF/CASCI object or RHF object
Returns:
A tuple, (natural orbitals, CI coefficients, orbital energies)
The orbital energies are the diagonal terms of general Fock matrix.
'''
from pyscf.lo import orth
from pyscf.tools import dump_mat
log = logger.new_logger(mc, verbose)
if mo_coeff is None: mo_coeff = mc.mo_coeff
if ci is None: ci = mc.ci
if casdm1 is None:
casdm1 = mc.fcisolver.make_rdm1(ci, mc.ncas, mc.nelecas)
ncore = mc.ncore
nocc = ncore + mc.ncas
nmo = mo_coeff.shape[1]
fock_ao = mc.get_fock(mo_coeff, ci, eris, casdm1, verbose)
fock = reduce(numpy.dot, (mo_coeff.T, fock_ao, mo_coeff))
mo_energy = fock.diagonal().copy()
if cas_natorb:
mo_coeff1, ci, occ = mc.cas_natorb(mo_coeff, ci, eris, sort, casdm1,
verbose)
ma = mo_coeff1[:,ncore:nocc]
mo_energy[ncore:nocc] = numpy.einsum('ji,ji->i', ma, fock_ao.dot(ma))
else:
# Keep the active space unchanged by default. The rotation in active space
# may cause problem for external CI solver eg DMRG.
mo_coeff1 = numpy.empty_like(mo_coeff)
mo_coeff1[:,ncore:nocc] = mo_coeff[:,ncore:nocc]
if ncore > 0:
# note the last two args of ._eig for mc1step_symm
# mc._eig function is called to handle symmetry adapated fock
w, c1 = mc._eig(fock[:ncore,:ncore], 0, ncore)
if sort:
idx = numpy.argsort(w.round(9), kind='mergesort')
w = w[idx]
c1 = c1[:,idx]
mo_coeff1[:,:ncore] = numpy.dot(mo_coeff[:,:ncore], c1)
mo_energy[:ncore] = w
if nmo-nocc > 0:
w, c1 = mc._eig(fock[nocc:,nocc:], nocc, nmo)
if sort:
idx = numpy.argsort(w.round(9), kind='mergesort')
w = w[idx]
c1 = c1[:,idx]
mo_coeff1[:,nocc:] = numpy.dot(mo_coeff[:,nocc:], c1)
mo_energy[nocc:] = w
if hasattr(mo_coeff, 'orbsym'):
if sort:
orbsym = symm.label_orb_symm(mc.mol, mc.mol.irrep_id,
mc.mol.symm_orb, mo_coeff1)
else:
orbsym = mo_coeff.orbsym
mo_coeff1 = lib.tag_array(mo_coeff1, orbsym=orbsym)
if log.verbose >= logger.DEBUG:
for i in range(nmo):
log.debug('i = %d <i|F|i> = %12.8f', i+1, mo_energy[i])
# still return ci coefficients, in case the canonicalization funciton changed
# cas orbitals, the ci coefficients should also be updated.
return mo_coeff1, ci, mo_energy
def get_nto(tdobj, state=1, threshold=OUTPUT_THRESHOLD, verbose=None):
r'''
Natural transition orbital analysis.
The natural transition density matrix between ground state and excited
state :math:`Tia = \langle \Psi_{ex} | i a^\dagger | \Psi_0 \rangle` can
be transformed to diagonal form through SVD
:math:`T = O \sqrt{\lambda} V^\dagger`. O and V are occupied and virtual
natural transition orbitals. The diagonal elements :math:`\lambda` are the
weights of the occupied-virtual orbital pair in the excitation.
Ref: Martin, R. L., JCP, 118, 4775-4777
Note in the TDHF/TDDFT calculations, the excitation part (X) is
interpreted as the CIS coefficients and normalized to 1. The de-excitation
part (Y) is ignored.
Args:
state : int
Excited state ID. state = 1 means the first excited state.
If state < 0, state ID is counted from the last excited state.
Kwargs:
threshold : float
Above which the NTO coefficients will be printed in the output.
Returns:
A list (weights, NTOs). NTOs are natural orbitals represented in AO
basis. The first N_occ NTOs are occupied NTOs and the rest are virtual
NTOs.
'''
if state == 0:
logger.warn(
tdobj, 'Excited state starts from 1. '
'Set state=1 for first excited state.')
state_id = state
elif state < 0:
state_id = state
else:
state_id = state - 1
mol = tdobj.mol
mo_coeff = tdobj._scf.mo_coeff
mo_occ = tdobj._scf.mo_occ
orbo_a = mo_coeff[0][:, mo_occ[0] == 1]
orbv_a = mo_coeff[0][:, mo_occ[0] == 0]
orbo_b = mo_coeff[1][:, mo_occ[1] == 1]
orbv_b = mo_coeff[1][:, mo_occ[1] == 0]
nocc_a = orbo_a.shape[1]
nvir_a = orbv_a.shape[1]
nocc_b = orbo_b.shape[1]
nvir_b = orbv_b.shape[1]
cis_t1a, cis_t1b = tdobj.xy[state_id][0]
norm = numpy.linalg.norm(cis_t1a)**2 + numpy.linalg.norm(cis_t1b)**2
cis_t1a *= 1. / norm
cis_t1b *= 1. / norm
if mol.symmetry:
orbsyma, orbsymb = uhf_symm.get_orbsym(mol, mo_coeff)
o_sym_a = orbsyma[mo_occ[0] == 1]
v_sym_a = orbsyma[mo_occ[0] == 0]
o_sym_b = orbsymb[mo_occ[1] == 1]
v_sym_b = orbsymb[mo_occ[1] == 0]
nto_o_a = numpy.eye(nocc_a)
nto_v_a = numpy.eye(nvir_a)
nto_o_b = numpy.eye(nocc_b)
nto_v_b = numpy.eye(nvir_b)
weights_o_a = numpy.zeros(nocc_a)
weights_v_a = numpy.zeros(nvir_a)
weights_o_b = numpy.zeros(nocc_b)
weights_v_b = numpy.zeros(nvir_b)
for ir in set(orbsyma):
idx = numpy.where(o_sym_a == ir)[0]
if idx.size > 0:
dm_oo = numpy.dot(cis_t1a[idx], cis_t1a[idx].T)
weights_o_a[idx], nto_o_a[idx[:, None],
idx] = numpy.linalg.eigh(dm_oo)
idx = numpy.where(v_sym_a == ir)[0]
if idx.size > 0:
dm_vv = numpy.dot(cis_t1a[:, idx].T, cis_t1a[:, idx])
weights_v_a[idx], nto_v_a[idx[:, None],
idx] = numpy.linalg.eigh(dm_vv)
for ir in set(orbsymb):
idx = numpy.where(o_sym_b == ir)[0]
if idx.size > 0:
dm_oo = numpy.dot(cis_t1b[idx], cis_t1b[idx].T)
weights_o_b[idx], nto_o_b[idx[:, None],
idx] = numpy.linalg.eigh(dm_oo)
idx = numpy.where(v_sym_b == ir)[0]
if idx.size > 0:
dm_vv = numpy.dot(cis_t1b[:, idx].T, cis_t1b[:, idx])
weights_v_b[idx], nto_v_b[idx[:, None],
idx] = numpy.linalg.eigh(dm_vv)
def sort(weights, nto, sym):
# weights in descending order
idx = numpy.argsort(-weights)
weights = weights[idx]
nto = nto[:, idx]
sym = sym[idx]
return weights, nto, sym
weights_o_a, nto_o_a, o_sym_a = sort(weights_o_a, nto_o_a, o_sym_a)
weights_v_a, nto_v_a, v_sym_a = sort(weights_v_a, nto_v_a, v_sym_a)
weights_o_b, nto_o_b, o_sym_b = sort(weights_o_b, nto_o_b, o_sym_b)
weights_v_b, nto_v_b, v_sym_b = sort(weights_v_b, nto_v_b, v_sym_b)
nto_orbsyma = numpy.hstack((o_sym_a, v_sym_a))
nto_orbsymb = numpy.hstack((o_sym_b, v_sym_b))
if nocc_a < nvir_a:
weights_a = weights_o_a
else:
weights_a = weights_v_a
if nocc_b < nvir_b:
weights_b = weights_o_b
else:
weights_b = weights_v_b
else:
nto_o_a, w_a, nto_v_aT = numpy.linalg.svd(cis_t1a)
nto_o_b, w_b, nto_v_bT = numpy.linalg.svd(cis_t1b)
nto_v_a = nto_v_aT.conj().T
nto_v_b = nto_v_bT.conj().T
weights_a = w_a**2
weights_b = w_b**2
nto_orbsyma = nto_orbsymb = None
def _set_phase_(c):
idx = numpy.argmax(abs(c.real), axis=0)
c[:, c[idx, numpy.arange(c.shape[1])].real < 0] *= -1
_set_phase_(nto_o_a)
_set_phase_(nto_o_b)
_set_phase_(nto_v_a)
_set_phase_(nto_v_b)
occupied_nto_a = numpy.dot(orbo_a, nto_o_a)
occupied_nto_b = numpy.dot(orbo_b, nto_o_b)
virtual_nto_a = numpy.dot(orbv_a, nto_v_a)
virtual_nto_b = numpy.dot(orbv_b, nto_v_b)
nto_coeff = (numpy.hstack((occupied_nto_a, virtual_nto_a)),
numpy.hstack((occupied_nto_b, virtual_nto_b)))
if mol.symmetry:
nto_coeff = (lib.tag_array(nto_coeff[0], orbsym=nto_orbsyma),
lib.tag_array(nto_coeff[1], orbsym=nto_orbsymb))
log = logger.new_logger(tdobj, verbose)
if log.verbose >= logger.INFO:
log.info('State %d: %g eV NTO largest component %s', state_id + 1,
tdobj.e[state_id] * nist.HARTREE2EV,
weights_a[0] + weights_b[0])
fmt = '%' + str(lib.param.OUTPUT_DIGITS) + 'f (MO #%d)'
o_idx_a = numpy.where(abs(nto_o_a[:, 0]) > threshold)[0]
v_idx_a = numpy.where(abs(nto_v_a[:, 0]) > threshold)[0]
o_idx_b = numpy.where(abs(nto_o_b[:, 0]) > threshold)[0]
v_idx_b = numpy.where(abs(nto_v_b[:, 0]) > threshold)[0]
log.info(' alpha occ-NTO: ' +
' + '.join([(fmt % (nto_o_a[i, 0], i + MO_BASE))
for i in o_idx_a]))
log.info(' alpha vir-NTO: ' +
' + '.join([(fmt % (nto_v_a[i, 0], i + MO_BASE + nocc_a))
for i in v_idx_a]))
log.info(' beta occ-NTO: ' +
' + '.join([(fmt % (nto_o_b[i, 0], i + MO_BASE))
for i in o_idx_b]))
log.info(' beta vir-NTO: ' +
' + '.join([(fmt % (nto_v_b[i, 0], i + MO_BASE + nocc_b))
for i in v_idx_b]))
return (weights_a, weights_b), nto_coeff
def get_veff(self, mol, dm, *args, **kwargs):
vhf = oldMF.get_veff(self, mol, dm)
epcm, vpcm = cosmo_solver(dm)
vhf += vpcm
return lib.tag_array(vhf, epcm=epcm, vpcm=vpcm)
def get_veff(ks, mol=None, dm=None, dm_last=0, vhf_last=0, hermi=1):
'''Coulomb + XC functional for GKS.
'''
if mol is None: mol = ks.mol
if dm is None: dm = ks.make_rdm1()
t0 = (time.clock(), time.time())
ground_state = (isinstance(dm, numpy.ndarray) and dm.ndim == 2)
assert (hermi == 1)
dm = numpy.asarray(dm)
nso = dm.shape[-1]
nao = nso // 2
dm_a = dm[..., :nao, :nao].real
dm_b = dm[..., nao:, nao:].real
if ks.grids.coords is None:
ks.grids.build(with_non0tab=True)
if ks.small_rho_cutoff > 1e-20 and ground_state:
ks.grids = rks.prune_small_rho_grids_(ks, mol, dm_a + dm_b,
ks.grids)
t0 = logger.timer(ks, 'setting up grids', *t0)
if ks.nlc != '':
if ks.nlcgrids.coords is None:
ks.nlcgrids.build(with_non0tab=True)
if ks.small_rho_cutoff > 1e-20 and ground_state:
ks.nlcgrids = rks.prune_small_rho_grids_(
ks, mol, dm_a + dm_b, ks.nlcgrids)
t0 = logger.timer(ks, 'setting up nlc grids', *t0)
max_memory = ks.max_memory - lib.current_memory()[0]
ni = ks._numint
n, exc, vxc = ni.nr_uks(mol,
ks.grids,
ks.xc, (dm_a, dm_b),
max_memory=max_memory)
if ks.nlc != '':
assert ('VV10' in ks.nlc.upper())
_, enlc, vnlc = ni.nr_rks(mol,
ks.nlcgrids,
ks.xc + '__' + ks.nlc,
dm_a + dm_b,
max_memory=max_memory)
exc += enlc
vxc += vnlc
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
if vxc.ndim == 4:
raise NotImplementedError
vxc = numpy.asarray(scipy.linalg.block_diag(*vxc), dtype=dm.dtype)
#enabling range-separated hybrids
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(ks.xc, spin=mol.spin)
if abs(hyb) < 1e-10 and abs(alpha) < 1e-10:
vk = None
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vj', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj = ks.get_j(mol, ddm, hermi)
vj += vhf_last.vj
else:
vj = ks.get_j(mol, dm, hermi)
vxc += vj
else:
if (ks._eri is None and ks.direct_scf
and getattr(vhf_last, 'vk', None) is not None):
ddm = numpy.asarray(dm) - numpy.asarray(dm_last)
vj, vk = ks.get_jk(mol, ddm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, ddm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vj += vhf_last.vj
vk += vhf_last.vk
else:
vj, vk = ks.get_jk(mol, dm, hermi)
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(mol, dm, hermi, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vxc += vj - vk
if ground_state:
exc -= numpy.einsum('ij,ji', dm, vk).real * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm, vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=vj, vk=vk)
return vxc
def get_veff(ks,
cell=None,
dm=None,
dm_last=0,
vhf_last=0,
hermi=1,
kpt=None,
kpts_band=None):
'''Coulomb + XC functional for UKS. See pyscf/pbc/dft/uks.py
:func:`get_veff` fore more details.
'''
if cell is None: cell = ks.cell
if dm is None: dm = ks.make_rdm1()
if kpt is None: kpt = ks.kpt
t0 = (time.clock(), time.time())
omega, alpha, hyb = ks._numint.rsh_and_hybrid_coeff(ks.xc, spin=cell.spin)
hybrid = abs(hyb) > 1e-10 or abs(alpha) > 1e-10
if not hybrid and isinstance(ks.with_df, multigrid.MultiGridFFTDF):
n, exc, vxc = multigrid.nr_uks(ks.with_df,
ks.xc,
dm,
hermi,
kpt.reshape(1, 3),
kpts_band,
with_j=True,
return_j=False)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
return vxc
# ndim = 3 : dm.shape = ([alpha,beta], nao, nao)
ground_state = (dm.ndim == 3 and dm.shape[0] == 2 and kpts_band is None)
if ks.grids.non0tab is None:
ks.grids.build(with_non0tab=True)
if (isinstance(ks.grids, gen_grid.BeckeGrids)
and ks.small_rho_cutoff > 1e-20 and ground_state):
ks.grids = rks.prune_small_rho_grids_(ks, cell, dm, ks.grids, kpt)
t0 = logger.timer(ks, 'setting up grids', *t0)
if not isinstance(dm, numpy.ndarray):
dm = numpy.asarray(dm)
if dm.ndim == 2: # RHF DM
dm = numpy.asarray((dm * .5, dm * .5))
if hermi == 2: # because rho = 0
n, exc, vxc = (0, 0), 0, 0
else:
n, exc, vxc = ks._numint.nr_uks(cell, ks.grids, ks.xc, dm, 0, kpt,
kpts_band)
logger.debug(ks, 'nelec by numeric integration = %s', n)
t0 = logger.timer(ks, 'vxc', *t0)
if not hybrid:
vj = ks.get_j(cell, dm[0] + dm[1], hermi, kpt, kpts_band)
vxc += vj
else:
if getattr(ks.with_df, '_j_only', False): # for GDF and MDF
ks.with_df._j_only = False
vj, vk = ks.get_jk(cell, dm, hermi, kpt, kpts_band)
vj = vj[0] + vj[1]
vk *= hyb
if abs(omega) > 1e-10:
vklr = ks.get_k(cell, dm, hermi, kpt, kpts_band, omega=omega)
vklr *= (alpha - hyb)
vk += vklr
vxc += vj - vk
if ground_state:
exc -= (numpy.einsum('ij,ji', dm[0], vk[0]) +
numpy.einsum('ij,ji', dm[1], vk[1])).real * .5
if ground_state:
ecoul = numpy.einsum('ij,ji', dm[0] + dm[1], vj).real * .5
else:
ecoul = None
vxc = lib.tag_array(vxc, ecoul=ecoul, exc=exc, vj=None, vk=None)
return vxc
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 = (logger.process_clock(), logger.perf_counter())
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 dia(magobj, gauge_orig=None):
mol = magobj.mol
mf = magobj._scf
mo_energy = magobj._scf.mo_energy
mo_coeff = magobj._scf.mo_coeff
mo_occ = magobj._scf.mo_occ
orboa = mo_coeff[0][:,mo_occ[0] > 0]
orbob = mo_coeff[1][:,mo_occ[1] > 0]
dm0a = lib.tag_array(orboa.dot(orboa.T), mo_coeff=mo_coeff[0], mo_occ=mo_occ[0])
dm0b = lib.tag_array(orbob.dot(orbob.T), mo_coeff=mo_coeff[1], mo_occ=mo_occ[1])
dm0 = dm0a + dm0b
dme0a = numpy.dot(orboa * mo_energy[0][mo_occ[0] > 0], orboa.T)
dme0b = numpy.dot(orbob * mo_energy[1][mo_occ[1] > 0], orbob.T)
dme0 = dme0a + dme0b
e2 = rhf_mag._get_dia_1e(magobj, gauge_orig, dm0, dme0)
if gauge_orig is not None:
return -e2
# Computing the 2nd order Vxc integrals from GIAO
grids = mf.grids
ni = mf._numint
xc_code = mf.xc
xctype = ni._xc_type(xc_code)
omega, alpha, hyb = ni.rsh_and_hybrid_coeff(xc_code, mol.spin)
make_rhoa, nset, nao = ni._gen_rho_evaluator(mol, dm0a, hermi=1)
make_rhob = ni._gen_rho_evaluator(mol, dm0b, hermi=1)[0]
ngrids = len(grids.weights)
mem_now = lib.current_memory()[0]
max_memory = max(2000, mf.max_memory*.9-mem_now)
BLKSIZE = numint.BLKSIZE
blksize = min(int(max_memory/12*1e6/8/nao/BLKSIZE)*BLKSIZE, ngrids)
vmata = numpy.zeros((3,3,nao,nao))
vmatb = numpy.zeros((3,3,nao,nao))
if xctype == 'LDA':
ao_deriv = 0
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory,
blksize=blksize):
rho = (make_rhoa(0, ao, mask, 'LDA'),
make_rhob(0, ao, mask, 'LDA'))
vxc = ni.eval_xc(xc_code, rho, spin=1, deriv=1)[1]
vrho = vxc[0]
r_ao = numpy.einsum('pi,px->pxi', ao, coords)
aow = numpy.einsum('pxi,p,p->pxi', r_ao, weight, vrho[:,0])
vmata += lib.einsum('pxi,pyj->xyij', r_ao, aow)
aow = numpy.einsum('pxi,p,p->pxi', r_ao, weight, vrho[:,1])
vmatb += lib.einsum('pxi,pyj->xyij', r_ao, aow)
rho = vxc = vrho = aow = None
elif xctype == 'GGA':
ao_deriv = 1
for ao, mask, weight, coords \
in ni.block_loop(mol, grids, nao, ao_deriv, max_memory,
blksize=blksize):
rho = (make_rhoa(0, ao, mask, 'GGA'),
make_rhob(0, ao, mask, 'GGA'))
vxc = ni.eval_xc(xc_code, rho, spin=1, deriv=1)[1]
wva, wvb = numint._uks_gga_wv0(rho, vxc, weight)
# Computing \nabla (r * AO) = r * \nabla AO + [\nabla,r]_- * AO
r_ao = numpy.einsum('npi,px->npxi', ao, coords)
r_ao[1,:,0] += ao[0]
r_ao[2,:,1] += ao[0]
r_ao[3,:,2] += ao[0]
aow = numpy.einsum('npxi,np->pxi', r_ao, wva)
vmata += lib.einsum('pxi,pyj->xyij', r_ao[0], aow)
aow = numpy.einsum('npxi,np->pxi', r_ao, wvb)
vmata += lib.einsum('pxi,pyj->xyij', r_ao[0], aow)
rho = vxc = vrho = aow = None
vmata = vmata + vmata.transpose(0,1,3,2)
vmatb = vmatb + vmatb.transpose(0,1,3,2)
elif xctype == 'MGGA':
raise NotImplementedError('meta-GGA')
vmata = rks_mag._add_giao_phase(mol, vmata)
vmatb = rks_mag._add_giao_phase(mol, vmatb)
e2 += numpy.einsum('qp,xypq->xy', dm0a, vmata)
e2 += numpy.einsum('qp,xypq->xy', dm0b, vmatb)
vmata = vmatb = None
e2 = e2.ravel()
# Handle the hybrid functional and the range-separated functional
if abs(hyb) > 1e-10:
vs = jk.get_jk(mol, [dm0, dm0a, dm0a, dm0b, dm0b],
['ijkl,ji->s2kl',
'ijkl,jk->s1il', 'ijkl,li->s1kj',
'ijkl,jk->s1il', 'ijkl,li->s1kj'],
'int2e_gg1', 's4', 9, hermi=1)
e2 += numpy.einsum('xpq,qp->x', vs[0], dm0)
e2 -= numpy.einsum('xpq,qp->x', vs[1], dm0a) * .5 * hyb
e2 -= numpy.einsum('xpq,qp->x', vs[2], dm0a) * .5 * hyb
e2 -= numpy.einsum('xpq,qp->x', vs[3], dm0b) * .5 * hyb
e2 -= numpy.einsum('xpq,qp->x', vs[4], dm0b) * .5 * hyb
vk = jk.get_jk(mol, [dm0a, dm0b], ['ijkl,jk->s1il', 'ijkl,jk->s1il'],
'int2e_g1g2', 'aa4', 9, hermi=0)
e2 -= numpy.einsum('xpq,qp->x', vk[0], dm0a) * hyb
e2 -= numpy.einsum('xpq,qp->x', vk[1], dm0b) * hyb
if abs(omega) > 1e-10:
with mol.with_range_coulomb(omega):
vs = jk.get_jk(mol, [dm0a, dm0a, dm0b, dm0b],
['ijkl,jk->s1il', 'ijkl,li->s1kj',
'ijkl,jk->s1il', 'ijkl,li->s1kj'],
'int2e_gg1', 's4', 9, hermi=1)
e2 -= numpy.einsum('xpq,qp->x', vs[0], dm0a) * .5 * (alpha-hyb)
e2 -= numpy.einsum('xpq,qp->x', vs[1], dm0a) * .5 * (alpha-hyb)
e2 -= numpy.einsum('xpq,qp->x', vs[2], dm0b) * .5 * (alpha-hyb)
e2 -= numpy.einsum('xpq,qp->x', vs[3], dm0b) * .5 * (alpha-hyb)
vk = jk.get_jk(mol, [dm0a, dm0b], ['ijkl,jk->s1il', 'ijkl,jk->s1il'],
'int2e_g1g2', 'aa4', 9, hermi=0)
e2 -= numpy.einsum('xpq,qp->x', vk[0], dm0a) * (alpha-hyb)
e2 -= numpy.einsum('xpq,qp->x', vk[1], dm0b) * (alpha-hyb)
else:
vj = jk.get_jk(mol, dm0, 'ijkl,ji->s2kl',
'int2e_gg1', 's4', 9, hermi=1)
e2 += numpy.einsum('xpq,qp->x', vj, dm0)
return -e2.reshape(3, 3)
def label_symmetry_(mc, mo_coeff, ci0=None):
log = logger.Logger(mc.stdout, mc.verbose)
#irrep_name = mc.mol.irrep_name
irrep_name = mc.mol.irrep_id
s = mc._scf.get_ovlp()
try:
orbsym = scf.hf_symm.get_orbsym(mc._scf.mol, mo_coeff, s, True)
except ValueError:
log.warn('mc1step_symm symmetrizes input orbitals')
ncore = mc.ncore
nocc = mc.ncore + mc.ncas
mo_cor = symm.symmetrize_space(mc.mol, mo_coeff[:, :ncore], s=s, check=False)
mo_act = symm.symmetrize_space(mc.mol, mo_coeff[:,ncore:nocc], s=s, check=False)
mo_vir = symm.symmetrize_space(mc.mol, mo_coeff[:,nocc: ], s=s, check=False)
mo_coeff = numpy.hstack((mo_cor,mo_act,mo_vir))
orbsym = symm.label_orb_symm(mc.mol, irrep_name,
mc.mol.symm_orb, mo_coeff, s=s)
mo_coeff_with_orbsym = lib.tag_array(mo_coeff, orbsym=orbsym)
active_orbsym = getattr(mc.fcisolver, 'orbsym', [])
if (not hasattr(active_orbsym, '__len__')) or len(active_orbsym) == 0:
ncore = mc.ncore
nocc = mc.ncore + mc.ncas
mc.fcisolver.orbsym = orbsym[ncore:nocc]
log.debug('Active space irreps %s', str(mc.fcisolver.orbsym))
wfnsym = 0
if getattr(mc.fcisolver, 'wfnsym', None) is not None:
wfnsym = mc.fcisolver.wfnsym
elif ci0 is None:
# Guess wfnsym based on HF determinant. mo_coeff may not be HF
# canonical orbitals. Some checks are needed to ensure that mo_coeff
# are derived from the symmetry adapted SCF calculations.
if mo_coeff is mc._scf.mo_coeff:
wfnsym = 0
for ir in orbsym[mc._scf.mo_occ == 1]:
wfnsym ^= ir
mc.fcisolver.wfnsym = wfnsym
log.debug('Set CASCI wfnsym %s based on HF determinant', wfnsym)
elif hasattr(mo_coeff, 'orbsym'): # It may be reordered SCF orbitals
ncore = mc.ncore
nocc = mc.ncore + mc.ncas
cas_orb = mo_coeff[:,ncore:nocc]
s = reduce(numpy.dot, (cas_orb.T, mc._scf.get_ovlp(), mc._scf.mo_coeff))
if numpy.all(numpy.max(s, axis=1) > 1-1e-9):
idx = numpy.argmax(s, axis=1)
cas_orbsym = orbsym[ncore:nocc]
cas_occ = mc._scf.mo_occ[idx]
wfnsym = 0
for ir in cas_orbsym[cas_occ == 1]:
wfnsym ^= ir
mc.fcisolver.wfnsym = wfnsym
log.debug('Active space are constructed from canonical SCF '
'orbitals %s', idx)
log.debug('Set CASCI wfnsym %s based on HF determinant', wfnsym)
elif hasattr(mc.fcisolver, 'guess_wfnsym'):
wfnsym = mc.fcisolver.guess_wfnsym(mc.ncas, mc.nelecas, ci0, verbose=log)
log.debug('CASCI wfnsym %s (based on CI initial guess)', wfnsym)
if isinstance(wfnsym, (int, numpy.integer)):
wfnsym = symm.irrep_id2name(mc.mol.groupname, wfnsym)
log.info('Active space CI wfn symmetry = %s', wfnsym)
return mo_coeff_with_orbsym
