def test_init(self):
hf = scf.RHF(mol)
ks = scf.RKS(mol)
kshf = scf.RKS(mol).set(xc='HF')
self.assertTrue(isinstance(tdscf.TDA(hf), tdscf.rhf.TDA))
self.assertTrue(isinstance(tdscf.TDA(ks), tdscf.rks.TDA))
self.assertTrue(isinstance(tdscf.TDA(kshf), tdscf.rks.TDA))
self.assertTrue(isinstance(tdscf.RPA(hf), tdscf.rhf.TDHF))
self.assertTrue(isinstance(tdscf.RPA(ks), tdscf.rks.TDDFTNoHybrid))
self.assertTrue(isinstance(tdscf.RPA(kshf), tdscf.rks.TDDFT))
self.assertTrue(isinstance(tdscf.TDDFT(hf), tdscf.rhf.TDHF))
self.assertTrue(isinstance(tdscf.TDDFT(ks), tdscf.rks.TDDFTNoHybrid))
self.assertTrue(isinstance(tdscf.TDDFT(kshf), tdscf.rks.TDDFT))
self.assertRaises(RuntimeError, tdscf.dRPA, hf)
self.assertTrue(isinstance(tdscf.dRPA(kshf), tdscf.rks.dRPA))
self.assertTrue(isinstance(tdscf.dRPA(ks), tdscf.rks.dRPA))
self.assertRaises(RuntimeError, tdscf.dTDA, hf)
self.assertTrue(isinstance(tdscf.dTDA(kshf), tdscf.rks.dTDA))
self.assertTrue(isinstance(tdscf.dTDA(ks), tdscf.rks.dTDA))
kshf.xc = ''
self.assertTrue(isinstance(tdscf.dTDA(kshf), tdscf.rks.dTDA))
self.assertTrue(isinstance(tdscf.dRPA(kshf), tdscf.rks.dRPA))
def test_nto(self):
mf = scf.UHF(mol).run()
td = tdscf.TDA(mf).run()
w, nto = td.get_nto(state=1)
self.assertAlmostEqual(w[0][0], 0.00018520143461015, 9)
self.assertAlmostEqual(w[1][0], 0.99963372674044326, 9)
self.assertAlmostEqual(lib.finger(w[0]), 0.00027305600430816, 9)
self.assertAlmostEqual(lib.finger(w[1]), 0.99964370569529093, 9)
pmol = copy.copy(mol)
pmol.symmetry = True
pmol.build(0, 0)
mf = scf.UHF(mol).run()
td = tdscf.TDA(mf).run()
w, nto = td.get_nto(state=0)
self.assertAlmostEqual(w[0][0], 0.00018520143461016, 9)
self.assertAlmostEqual(w[1][0], 0.99963372674044326, 9)
self.assertAlmostEqual(lib.finger(w[0]), 0.00027305600430816, 9)
self.assertAlmostEqual(lib.finger(w[1]), 0.99964370569529093, 9)
def test_tda_b88(self):
td = tdscf.TDA(mf_gga).run(nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[0, 2], -0.8120037135120326, 6)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual((e1[2] - e2[2]) / .002, g1[0, 2], 5)
def run_pyscf_tdscf(xyz,
basis,
charge=0,
multiplicity=1,
conv_tol=1e-12,
conv_tol_grad=1e-11,
max_iter=150,
pcm_options=None):
mol = gto.M(
atom=xyz,
basis=basis,
unit="Bohr",
charge=charge,
# spin in the pyscf world is 2S
spin=multiplicity - 1,
verbose=0,
# Disable commandline argument parsing in pyscf
parse_arg=False,
dump_input=False,
)
if pcm_options:
mf = ddCOSMO(scf.RHF(mol))
mf.with_solvent.eps = pcm_options.get("eps")
else:
mf = scf.RHF(mol)
mf.conv_tol = conv_tol
mf.conv_tol_grad = conv_tol_grad
mf.max_cycle = max_iter
mf.kernel()
if pcm_options:
mf.with_solvent.eps = pcm_options.get("eps_opt")
# for n_eq solvation only PTE implemented
mf.with_solvent.equilibrium_solvation = True
cis = ddCOSMO(tdscf.TDA(mf))
else:
cis = tdscf.TDA(mf)
cis.nstates = 5
cis.conv_tol = 1e-7
cis.kernel()
return mf, cis
def test_tda_triplet(self):
td = tdscf.TDA(mf).run(singlet=False, nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[0, 2], -0.47296513687621511, 8)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual((e1[2] - e2[2]) / .002, g1[0, 2], 5)
def test_tda(self):
td = tdscf.TDA(mf).run(nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[0,2], -0.78246882668628404, 7)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual((e1[2]-e2[2])/.002, g1[0,2], 4)
def test_tda_lda(self):
td = tdscf.TDA(mf_lda).run(nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(td.xy[2])
self.assertAlmostEqual(g1[0, 2], -0.40279473514282405, 6)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual((e1[2] - e2[2]) / .002, g1[0, 2], 5)
def test_tda_triplet_lda(self):
td = tdscf.TDA(mf_lda).run(singlet=False, nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[0,2], -0.3633334, 8)
td_solver = td.as_scanner()
pmol = mol.copy()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual(abs((e1[2]-e2[2])/.002 - g1[0,2]).max(), 0, 5)
def test_tda_lda(self):
td = tdscf.TDA(mf_lda).set(conv_tol=1e-12)
es = td.kernel(nstates=5)[0] * 27.2114
self.assertAlmostEqual(lib.fp(es[:3]), 1.4581538269747121, 6)
ref = [2.14644585, 3.27738191, 5.90913787, 12.14980714, 13.15535042]
self.assertAlmostEqual(abs(es - ref).max(), 0, 5)
mf = dft.UKS(mol1).run(xc='lda,vwn').run()
td = mf.TDA()
td.nstates = 5
es = td.kernel()[0] * 27.2114
ref = [6.88046608, 7.58244885, 8.49961771, 9.30209259, 9.79775972]
self.assertAlmostEqual(abs(es - ref).max(), 0, 5)
def test_tda_singlet(self):
td = tdscf.TDA(mf).run(nstates=3)
tdg = td.nuc_grad_method().as_scanner()
g1 = tdg(mol.atom_coords(), state=3)[1]
self.assertAlmostEqual(g1[0, 2], -0.23226123352352346, 8)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; F 0 0 0', unit='B'))
e2 = td_solver(pmol.set_geom_('H 0 0 1.803; F 0 0 0', unit='B'))
self.assertAlmostEqual((e1[2] - e2[2]) / .002, g1[0, 2], 6)
self.assertAlmostEqual(
abs(tdg.kernel(state=0) - mf.nuc_grad_method().kernel()).max(), 0,
8)
def test_nto(self):
mf = mf_uhf
td = tdscf.TDA(mf).run()
w, nto = td.get_nto(state=1)
self.assertAlmostEqual(w[0][0], 0.00018520143461015, 9)
self.assertAlmostEqual(w[1][0], 0.99963372674044326, 9)
self.assertAlmostEqual(lib.fp(w[0]), 0.00027305600430816, 9)
self.assertAlmostEqual(lib.fp(w[1]), 0.99964370569529093, 9)
pmol = copy.copy(mol)
pmol.symmetry = True
pmol.build(0, 0)
mf = scf.UHF(pmol).run()
td = tdscf.TDA(mf).run(nstates=3)
w, nto = td.get_nto(state=0)
self.assertAlmostEqual(w[0][0], 0.00018520143461016, 9)
self.assertAlmostEqual(w[1][0], 0.99963372674044326, 9)
self.assertAlmostEqual(lib.fp(w[0]), 0.00027305600430816, 9)
self.assertAlmostEqual(lib.fp(w[1]), 0.99964370569529093, 9)
w, nto = td.get_nto(state=-1)
self.assertAlmostEqual(w[0][0], 0.00236940007134660, 9)
self.assertAlmostEqual(w[1][0], 0.99759687228056182, 9)
def test_tda_grad(self):
mol0 = gto.M(atom='H 0 0 0 ; H .5 .5 .1', unit='B', basis='321g')
mol1 = gto.M(atom='H 0 0 -.001; H .5 .5 .1', unit='B', basis='321g')
mol2 = gto.M(atom='H 0 0 0.001; H .5 .5 .1', unit='B', basis='321g')
mf = scf.RHF(mol0).ddCOSMO().run()
td = solvent.ddCOSMO(tdscf.TDA(mf)).run(equilibrium_solvation=True)
ref = tda_grad(td, td.xy[0]) + mf.nuc_grad_method().kernel()
e, de = td.nuc_grad_method().as_scanner(state=1)(mol0)
de = td.nuc_grad_method().kernel()
self.assertAlmostEqual(abs(ref - de).max(), 0, 12)
td1 = mol1.RHF().ddCOSMO().run().TDA().ddCOSMO().run(
equilibrium_solvation=True)
td2 = mol2.RHF().ddCOSMO().run().TDA().ddCOSMO().run(
equilibrium_solvation=True)
e1 = td1.e_tot[0]
e2 = td2.e_tot[0]
self.assertAlmostEqual((e2 - e1) / 0.002, de[0, 2], 5)
def test_tda_lda(self):
td = tdscf.TDA(mf_lda).set(conv_tol=1e-12)
es = td.kernel(nstates=4)[0] * 27.2114
self.assertAlmostEqual(lib.finger(es[:3]), 1.4581538269747121, 6)
def test_tda_b3lyp(self):
td = tdscf.TDA(mf_b3lyp).set(conv_tol=1e-12)
es = td.kernel(nstates=4)[0] * 27.2114
self.assertAlmostEqual(lib.finger(es[:3]), 1.4303636271767162, 6)
def test_tda_singlet_b88(self):
td = tdscf.TDA(mf_gga).run(nstates=3)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[0,2], -9.32506535e-02, 8)
def test_tda_singlet_lda(self):
td = tdscf.TDA(mf_lda).run(nstates=3)
tdg = td.nuc_grad_method()
g1 = tdrks_grad.kernel(tdg, td.xy[2])
g1 += tdg.grad_nuc()
self.assertAlmostEqual(g1[0,2], -9.23916667e-02, 8)
''' The default XC functional library (libxc) supports the energy and nuclear gradients for range separated functionals. Nuclear Hessian and TDDFT gradients need xcfun library. See also example 32-xcfun_as_default.py for how to set xcfun library as the default XC functional library. ''' from pyscf import gto, dft mol = gto.M(atom="H; F 1 1.", basis='631g') mf = dft.UKS(mol) mf.xc = 'CAMB3LYP' mf.kernel() mf.nuc_grad_method().kernel() from pyscf.hessian import uks as uks_hess # Switching to xcfun library on the fly mf._numint.libxc = dft.xcfun hess = uks_hess.Hessian(mf).kernel() print(hess.reshape(2, 3, 2, 3)) from pyscf import tdscf # Switching to xcfun library on the fly mf._numint.libxc = dft.xcfun tdks = tdscf.TDA(mf) tdks.nstates = 3 tdks.kernel() tdks.nuc_grad_method().kernel()
CIS excited state density with TDA amplitudes
'''
import numpy as np
from pyscf import gto, dft, tdscf
mol = gto.M(
atom='H 0 0 0; F 0 0 1.1',
basis='631g',
)
mf = dft.RKS(mol)
mf.xc = 'b3lyp'
mf.kernel()
mytd = tdscf.TDA(mf).run(nstates=3)
#mytd.analyze()
def tda_denisty_matrix(td, state_id):
'''
Taking the TDA amplitudes as the CIS coefficients, calculate the density
matrix (in AO basis) of the excited states
'''
cis_t1 = td.xy[state_id][0]
dm_oo = -np.einsum('ia,ka->ik', cis_t1.conj(), cis_t1)
dm_vv = np.einsum('ia,ic->ac', cis_t1, cis_t1.conj())
# The ground state density matrix in mo_basis
mf = td._scf
dm = np.diag(mf.mo_occ)
