def test_evolve(mol_num, j_constant_value, elocalex_value, ph_info,
ph_phys_dim, evolve_dt, nsteps):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList(
[Mol(Quantity(elocalex_value, "a.u."), ph_list)] * mol_num,
Quantity(j_constant_value, "eV"),
scheme=3,
)
ct1 = ChargeTransport(mol_list, stop_at_edge=False)
half_nsteps = nsteps // 2
ct1.evolve(evolve_dt, half_nsteps)
ct1.evolve(evolve_dt, nsteps - half_nsteps)
ct2 = ChargeTransport(mol_list, stop_at_edge=False)
ct2.evolve(evolve_dt, nsteps)
assert ct1.is_similar(ct2)
assert_iterable_equal(ct1.get_dump_dict(), ct2.get_dump_dict())
# test dump
ct2.dump_dir = "."
ct2.job_name = "test"
ct2.dump_dict()
os.remove("test.npz")
def test_band_limit_finite_t(
mol_num,
j_constant_value,
elocalex_value,
ph_info,
ph_phys_dim,
evolve_dt,
nsteps,
scheme,
):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList(
[Mol(Quantity(elocalex_value, "a.u."), ph_list)] * mol_num,
Quantity(j_constant_value, "eV"),
scheme=scheme,
)
ct1 = ChargeTransport(mol_list, stop_at_edge=False)
ct1.evolve(evolve_dt, nsteps)
ct2 = ChargeTransport(mol_list, temperature=low_t, stop_at_edge=False)
ct2.evolve(evolve_dt, nsteps)
assert ct1.is_similar(ct2)
def test_reduced_density_matrix(
mol_num,
j_constant_value,
elocalex_value,
ph_info,
ph_phys_dim,
evolve_dt,
nsteps,
temperature,
):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList([Mol(Quantity(elocalex_value, "a.u."), ph_list)] *
mol_num,
Quantity(j_constant_value, "eV"),
scheme=3)
ct = ChargeTransport(mol_list,
temperature=Quantity(temperature, "K"),
stop_at_edge=False,
rdm=True)
ct.evolve(evolve_dt, nsteps)
for rdm, e in zip(ct.reduced_density_matrices, ct.e_occupations_array):
# best we can do?
assert np.allclose(np.diag(rdm), e)
def test_autocorr(scheme, mollist2):
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
mol = Mol(Quantity(0), [ph])
mol_list1 = MolList([mol] * 5, Quantity(1), scheme)
if mollist2:
mol_list = MolList2.MolList_to_MolList2(mol_list1)
else:
mol_list = mol_list1
temperature = Quantity(50000, 'K')
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=24)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=0.5,
adaptive_rtol=1e-3)
ievolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=-0.1j)
ac = TransportAutoCorr(mol_list,
temperature,
compress_config=compress_config,
ievolve_config=ievolve_config,
evolve_config=evolve_config)
ac.evolve(nsteps=5, evolve_time=5)
corr_real = ac.auto_corr.real
exact_real = get_exact_autocorr(mol_list1, temperature,
ac.evolve_times_array).real
atol = 1e-2
# direct comparison may fail because of different sign
assert np.allclose(corr_real, exact_real, atol=atol) or np.allclose(
corr_real, -exact_real, atol=atol)
def custom_mol_list(
custom_j_matrix=None,
n_phys_dim=None,
nqboson=None,
qbtrunc=None,
force3rd=None,
dis=None,
hartrees=None,
nmols=3,
) -> MolList:
if custom_j_matrix is None:
custom_j_matrix = _j_matrix
if n_phys_dim is None:
n_phys_dim = ph_phys_dim
if nqboson is None:
nqboson = [1, 1]
if qbtrunc is None:
qbtrunc = [0.0, 0.0]
if force3rd is None:
force3rd = [None, None]
if dis is None:
dis = displacement_quantities
if hartrees is None:
hartrees = [False, False]
displacement = [[Quantity(0), dis[0]], [Quantity(0), dis[1]]]
ph_list = [
Phonon(*args) for args in zip(omega, displacement, n_phys_dim,
force3rd, nqboson, qbtrunc, hartrees)
]
return MolList([Mol(elocalex, ph_list, dipole_abs)] * nmols,
custom_j_matrix)
def test_scheme4():
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m1 = Mol(Quantity(0), [ph])
m2 = Mol(Quantity(0), [ph]*2)
mlist1 = MolList([m1, m2], Quantity(17), 4)
mlist2 = MolList([m1, m2], Quantity(17), 3)
mpo4 = Mpo(mlist1)
assert mpo4.is_hermitian()
# for debugging
f = mpo4.full_operator()
mpo3 = Mpo(mlist2)
assert mpo3.is_hermitian()
# makeup two states
mps4 = Mps()
mps4.mol_list = mlist1
mps4.use_dummy_qn = True
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.append(np.array([0, 1]).reshape((1,2,1)))
mps4.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.build_empty_qn()
e4 = mps4.expectation(mpo4)
mps3 = Mps()
mps3.mol_list = mlist2
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([0, 1]).reshape((1,2,1)))
mps3.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
e3 = mps3.expectation(mpo3)
assert pytest.approx(e4) == e3
def test_scheme4():
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m1 = Mol(Quantity(0), [ph])
m2 = Mol(Quantity(0), [ph] * 2)
model4 = HolsteinModel([m1, m2], Quantity(17), 4)
model3 = HolsteinModel([m1, m2], Quantity(17), 3)
mpo4 = Mpo(model4)
assert mpo4.is_hermitian()
# for debugging
f = mpo4.full_operator()
mpo3 = Mpo(model3)
assert mpo3.is_hermitian()
# makeup two states
mps4 = Mps()
mps4.model = model4
mps4.append(np.array([1, 0]).reshape((1, 2, 1)))
mps4.append(np.array([0, 0, 1]).reshape((1, -1, 1)))
mps4.append(np.array([0.707, 0.707]).reshape((1, 2, 1)))
mps4.append(np.array([1, 0]).reshape((1, 2, 1)))
mps4.build_empty_qn()
e4 = mps4.expectation(mpo4)
mps3 = Mps()
mps3.model = model3
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
mps3.append(np.array([0, 1]).reshape((1, 2, 1)))
mps3.append(np.array([0.707, 0.707]).reshape((1, 2, 1)))
mps3.append(np.array([1, 0]).reshape((1, 2, 1)))
e3 = mps3.expectation(mpo3)
assert pytest.approx(e4) == e3
def test_zt_init_state():
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 10)
mol_list = MolList([Mol(Quantity(0), [ph])], Quantity(0), scheme=3)
mpo = Mpo(mol_list)
mps = Mps.random(mol_list, 1, 10)
optimize_mps(mps, mpo)
ct = ChargeTransport(mol_list)
assert mps.angle(ct.latest_mps) == pytest.approx(1)
def get_model():
nphonons = 5
ph_levels = 2
epsilon = 1
delta = 1
ph_list = [Phonon.simple_phonon(Quantity(1), Quantity(1), ph_levels)
] * nphonons
return SpinBosonModel(Quantity(epsilon), Quantity(delta), ph_list)
def test_bogoliubov():
# REF: JCP, 2016, 145, 224101
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps)
# evolve_config = EvolveConfig()
omega = 1
D = 1
nlevel = 10
T = Quantity(1)
ph1 = Phonon.simple_phonon(Quantity(omega), Quantity(D), nlevel)
mol1 = Mol(Quantity(0), [ph1])
mlist = MolList([mol1] * 2, Quantity(1), scheme=4)
mpdm1 = MpDm.max_entangled_gs(mlist)
mpdm1.evolve_config = evolve_config
mpo1 = Mpo(mlist)
tp = ThermalProp(mpdm1, mpo1, exact=True)
tp.evolve(nsteps=20, evolve_time=T.to_beta() / 2j)
mpdm2 = tp.latest_mps
e1 = mpdm2.expectation(mpo1)
mpdm3 = (Mpo.onsite(mlist, r"a^\dagger", False, {0})
@ mpdm2).expand_bond_dimension(mpo1)
es1 = [mpdm3.e_occupations]
for i in range(40):
mpdm3 = mpdm3.evolve(mpo1, 0.1)
es1.append(mpdm3.e_occupations)
theta = np.arctanh(np.exp(-T.to_beta() * omega / 2))
ph2 = Phonon.simple_phonon(Quantity(omega), Quantity(D * np.cosh(theta)),
nlevel)
ph3 = Phonon.simple_phonon(Quantity(-omega), Quantity(-D * np.sinh(theta)),
nlevel)
mol2 = Mol(Quantity(0), [ph2, ph3])
mlist2 = MolList([mol2] * 2, Quantity(1), scheme=4)
mps1 = Mps.gs(mlist2, False)
mps1.evolve_config = evolve_config
mpo2 = Mpo(mlist2)
e2 = mps1.expectation(mpo2)
mps2 = (Mpo.onsite(mlist2, r"a^\dagger", False, {0})
@ mps1).expand_bond_dimension(mpo2)
es2 = [mps2.e_occupations]
for i in range(20):
mps2 = mps2.evolve(mpo2, 0.2)
es2.append(mps2.e_occupations)
assert np.allclose(es1[::2], es2, atol=5e-3)
def test_sdf():
alpha = 0.05
omega_c = Quantity(5)
sdf = SpectralDensityFunction(alpha, omega_c)
omega_list, displacement_list = sdf.trapz(200, 0.0, 50)
ph_list = [Phonon.simplest_phonon(o, d) for o,d in zip(omega_list, displacement_list)]
mol_reor = sum(ph.reorganization_energy.as_au() for ph in ph_list)
assert mol_reor == pytest.approx(alpha * omega_c.as_au() / 2, abs=0.005)
def get_mol():
nphonons = 5
ph_levels = 2
delta = 1
epsilon = 1
ph_list = [Phonon.simple_phonon(Quantity(1), Quantity(1), ph_levels)
] * nphonons
m = Mol(Quantity(epsilon), ph_list, tunnel=Quantity(-delta))
return m
def test_ft_init_state():
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 10)
mol_list = MolList([Mol(Quantity(0), [ph])], Quantity(0), scheme=3)
temperature = Quantity(0.1)
mpo = Mpo(mol_list)
init_mpdm = MpDm.max_entangled_ex(mol_list)
tp = ThermalProp(init_mpdm, mpo, space="EX", exact=True)
tp.evolve(nsteps=20, evolve_time=temperature.to_beta() / 2j)
ct = ChargeTransport(mol_list, temperature=temperature)
tp_mpdm = MpDmFull.from_mpdm(tp.latest_mps)
ct_mpdm = MpDmFull.from_mpdm(ct.latest_mps)
assert tp_mpdm.angle(ct_mpdm) == pytest.approx(1)
def test_offset(scheme):
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m = Mol(Quantity(0), [ph] * 2)
mlist = MolList([m] * 2, Quantity(17), scheme=scheme)
mpo1 = Mpo(mlist)
assert mpo1.is_hermitian()
f1 = mpo1.full_operator()
evals1, _ = np.linalg.eigh(f1.asnumpy())
offset = Quantity(0.123)
mpo2 = Mpo(mlist, offset=offset)
f2 = mpo2.full_operator()
evals2, _ = np.linalg.eigh(f2.asnumpy())
assert np.allclose(evals1 - offset.as_au(), evals2)
def param2mollist(alpha: float, raw_delta: Quantity, omega_c: Quantity,
renormalization_p: float, n_phonons: int):
sdf = SpectralDensityFunction(alpha, omega_c)
delta, max_omega = sdf.adiabatic_renormalization(raw_delta,
renormalization_p)
omega_list, displacement_list = sdf.trapz(n_phonons, 0.0,
max_omega.as_au())
ph_list = [
Phonon.simplest_phonon(o, d)
for o, d in zip(omega_list, displacement_list)
]
return SpinBosonModel(Quantity(0), delta, ph_list)
def test_similar(mol_num, j_constant_value, elocalex_value, ph_info,
ph_phys_dim, evolve_dt, nsteps):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList([Mol(Quantity(elocalex_value, "a.u."), ph_list)] *
mol_num,
Quantity(j_constant_value, "eV"),
scheme=3)
ct1 = ChargeTransport(mol_list)
ct1.evolve(evolve_dt, nsteps)
ct2 = ChargeTransport(mol_list)
ct2.evolve(evolve_dt + 1e-5, nsteps)
assert ct1.is_similar(ct2)
def test_autocorr(insteps, atol):
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
mol = Mol(Quantity(0), [ph])
mol_list = MolList([mol] * 5, Quantity(1), 3)
temperature = Quantity(50000, 'K')
compress_config = CompressConfig(threshold=1e-3)
ac = TransportAutoCorr(mol_list,
temperature,
insteps,
compress_config=compress_config)
ac.evolve(0.2, 50)
corr_real = ac.auto_corr.real
exact_real = get_exact_autocorr(mol_list, temperature,
ac.evolve_times_array).real
# direct comparison may fail because of different sign
assert np.allclose(corr_real, exact_real, atol=atol) or np.allclose(
corr_real, -exact_real, atol=atol)
def custom_model(
custom_j_matrix=None,
n_phys_dim=None,
dis=None,
nmols=3,
) -> HolsteinModel:
if custom_j_matrix is None:
custom_j_matrix = _j_matrix
if n_phys_dim is None:
n_phys_dim = ph_phys_dim
if dis is None:
dis = displacement_quantities
displacement = [[Quantity(0), dis[0]], [Quantity(0), dis[1]]]
ph_list = [Phonon(*args) for args in zip(omega, displacement, n_phys_dim)]
return HolsteinModel(
[Mol(elocalex, ph_list, dipole_abs)] * nmols,
custom_j_matrix,
)
def test_scheme4_finite_t(mol_num, j_constant_value, elocalex_value, ph_info,
ph_phys_dim, evolve_dt, nsteps):
temperature = Quantity(1, "a.u.")
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList([Mol(Quantity(elocalex_value, "a.u."), ph_list)] *
mol_num, Quantity(j_constant_value, "eV"))
ct1 = ChargeTransport(mol_list.switch_scheme(3),
temperature=temperature,
stop_at_edge=False)
ct1.evolve(evolve_dt, nsteps)
ct2 = ChargeTransport(mol_list.switch_scheme(4),
temperature=temperature,
stop_at_edge=False)
ct2.evolve(evolve_dt, nsteps)
assert ct1.is_similar(ct2, rtol=1e-2)
def test_compress_add(mol_num, j_constant_value, elocalex_value, ph_info,
ph_phys_dim, evolve_dt, nsteps):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list = MolList([Mol(Quantity(elocalex_value, "a.u."), ph_list)] *
mol_num,
Quantity(j_constant_value, "eV"),
scheme=3)
ct1 = ChargeTransport(mol_list, temperature=Quantity(298, "K"))
ct1.reduced_density_matrices = None
ct1.evolve(evolve_dt, nsteps)
ct2 = ChargeTransport(mol_list, temperature=Quantity(298, "K"))
ct2.reduced_density_matrices = None
ct2.latest_mps.compress_add = True
ct2.evolve(evolve_dt, nsteps)
assert ct1.is_similar(ct2, rtol=1e-2)
def test_mpdm_full(nmols, phonon_freq):
ph = Phonon.simple_phonon(Quantity(phonon_freq), Quantity(1), 2)
m = Mol(Quantity(0), [ph])
mol_list = MolList([m] * nmols, Quantity(1))
gs_dm = MpDm.max_entangled_gs(mol_list)
beta = Quantity(1000, "K").to_beta()
tp = ThermalProp(gs_dm, Mpo(mol_list), exact=True, space="GS")
tp.evolve(None, 50, beta / 1j)
gs_dm = tp.latest_mps
assert np.allclose(gs_dm.e_occupations, [0] * nmols)
e_gs_dm = Mpo.onsite(mol_list, r"a^\dagger",
mol_idx_set={0}).apply(gs_dm, canonicalise=True)
assert np.allclose(e_gs_dm.e_occupations, [1] + [0] * (nmols - 1))
mpdm_full = MpDmFull.from_mpdm(e_gs_dm)
assert np.allclose(mpdm_full.e_occupations, e_gs_dm.e_occupations)
assert np.allclose(mpdm_full.ph_occupations,
e_gs_dm.ph_occupations,
rtol=1e-3)
def test_2site():
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
m = Mol(Quantity(0), [ph])
mol_list = MolList([m] * 2, Quantity(1), scheme=3)
gs_mp = Mpo.onsite(mol_list, opera=r"a^\dagger", mol_idx_set={0}).apply(Mps.gs(mol_list, max_entangled=False))
mpdm = MpDm.from_mps(gs_mp)
mpdm_full = MpDmFull.from_mpdm(mpdm)
mpdm_full.compress_config = CompressConfig(threshold=1e-4)
liouville = SuperLiouville(Mpo(mol_list), dissipation=1)
ph_occupations_array = []
energies = []
for i in range(51):
logger.info(mpdm_full)
logger.info(mpdm_full.ph_occupations)
ph_occupations_array.append(mpdm_full.ph_occupations)
logger.info(mpdm_full.expectation(liouville))
energies.append(mpdm_full.expectation(liouville))
mpdm_full = mpdm_full.evolve(liouville, 0.4)
ph_occupations_array = np.array(ph_occupations_array)
assert energies[-1] == pytest.approx(-0.340162, rel=1e-2)
assert np.allclose(ph_occupations_array[-1], [0.0930588, 0.099115], rtol=1e-2)
def construct_model(nmols, dmrg_nphs, hartree_nphs) -> HolsteinModel:
assert dmrg_nphs + hartree_nphs == 10
elocalex = Quantity(2.13 / constant.au2ev)
dipole_abs = 1.0
# cm^-1
omega_value = (np.array([
206.0, 211.0, 540.0, 552.0, 751.0, 1325.0, 1371.0, 1469.0, 1570.0,
1628.0
]) * constant.cm2au)
S_value = np.array(
[0.197, 0.215, 0.019, 0.037, 0.033, 0.010, 0.208, 0.042, 0.083, 0.039])
# sort from large to small
gw = np.sqrt(S_value) * omega_value
idx = np.argsort(gw)[::-1]
omega_value = omega_value[idx]
S_value = S_value[idx]
omega = [[Quantity(x), Quantity(x)] for x in omega_value]
D_value = np.sqrt(S_value) / np.sqrt(omega_value / 2.0)
displacement = [[Quantity(0), Quantity(x)] for x in D_value]
ph_phys_dim = [5] * 10
# print(dmrg_nphs, hartree_nphs)
is_hartree = [False] * dmrg_nphs + [True] * hartree_nphs
ph_list = [
Phonon(*args[:3], hartree=args[3])
for args in zip(omega, displacement, ph_phys_dim, is_hartree)
]
model = HolsteinModel(
[Mol(elocalex, ph_list, dipole_abs)] * nmols,
Quantity(500, "cm-1"),
)
return model
def test_reduced_density_matrix(
mol_num,
j_constant_value,
elocalex_value,
ph_info,
ph_phys_dim,
evolve_dt,
nsteps,
temperature,
):
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"),
Quantity(displacement, "a.u."), ph_phys_dim)
for omega, displacement in ph_info
]
mol_list3 = MolList(
[Mol(Quantity(elocalex_value, "a.u."), ph_list)] * mol_num,
Quantity(j_constant_value, "eV"),
scheme=3,
)
ct3 = ChargeTransport(mol_list3,
temperature=Quantity(temperature, "K"),
stop_at_edge=False,
rdm=True)
ct3.evolve(evolve_dt, nsteps)
mol_list4 = mol_list3.switch_scheme(4)
ct4 = ChargeTransport(mol_list4,
temperature=Quantity(temperature, "K"),
stop_at_edge=False,
rdm=True)
ct4.evolve(evolve_dt, nsteps)
for rdm3, rdm4, e in zip(ct3.reduced_density_matrices,
ct4.reduced_density_matrices,
ct3.e_occupations_array):
assert np.allclose(rdm3, rdm4, atol=1e-3)
assert np.allclose(np.diag(rdm3), e)
def test_holstein_kubo(scheme):
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
mol = Mol(Quantity(0), [ph])
model = HolsteinModel([mol] * 5, Quantity(1), scheme)
temperature = Quantity(50000, 'K')
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=24)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=0.5,
adaptive_rtol=1e-3)
ievolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=-0.1j)
kubo = TransportKubo(model,
temperature,
compress_config=compress_config,
ievolve_config=ievolve_config,
evolve_config=evolve_config)
kubo.evolve(nsteps=5, evolve_time=5)
qutip_res = get_qutip_holstein_kubo(model, temperature,
kubo.evolve_times_array)
rtol = 5e-2
assert np.allclose(kubo.auto_corr, qutip_res, rtol=rtol)
N_PHONONS = 35
TOTAL_HR = 0.42
if __name__ == "__main__":
omegas_cm = np.linspace(2, 300, N_PHONONS)
omegas_au = omegas_cm * cm2au
hr_factors = np.interp(omegas_cm, sdf_values[:, 0], sdf_values[:, 1])
hr_factors *= TOTAL_HR / hr_factors.sum()
lams = hr_factors * omegas_au
phonons = [
Phonon.simplest_phonon(Quantity(o), Quantity(l), lam=True)
for o, l in zip(omegas_au, lams)
]
j_matrix_au = j_matrix_cm * cm2au
mlist = []
for j in np.diag(j_matrix_au):
m = Mol(Quantity(j), phonons)
mlist.append(m)
# starts from 1
mol_arangement = np.array([7, 5, 3, 1, 2, 4, 6]) - 1
mol_list = MolList(list(np.array(mlist)[mol_arangement]),
j_matrix_au[mol_arangement][:, mol_arangement])
_j_matrix = (
np.array([[0.0, -0.1, -0.2], [-0.1, 0.0, -0.3], [-0.2, -0.3, 0.0]]) /
constant.au2ev)
omega_quantities = [Quantity(106.51, "cm^{-1}"), Quantity(1555.55, "cm^{-1}")]
omega = [
[omega_quantities[0], omega_quantities[0]],
[omega_quantities[1], omega_quantities[1]],
]
displacement_quantities = [Quantity(30.1370, "a.u."), Quantity(8.7729, "a.u.")]
displacement = [
[Quantity(0), displacement_quantities[0]],
[Quantity(0), displacement_quantities[1]],
]
ph_phys_dim = [4, 4]
ph_list = [Phonon(*args) for args in zip(omega, displacement, ph_phys_dim)]
hartree_ph_list = [
Phonon(*args, hartree=True)
for args in zip(omega, displacement, ph_phys_dim)
]
hybrid_ph_list = [ph_list[1], hartree_ph_list[0]]
mol_list = MolList([Mol(elocalex, ph_list, dipole_abs)] * nmols, _j_matrix)
hartree_mol_list = MolList([Mol(elocalex, hartree_ph_list, dipole_abs)] *
nmols, _j_matrix)
hybrid_mol_list = MolList([Mol(elocalex, hybrid_ph_list, dipole_abs)] * nmols,
_j_matrix)
offset = Quantity(2.28614053, "ev")
logger.info(f"g:{g_value}")
logger.info(
f"lambda:{lambda_value*constant.au2ev}ev,{lambda_value*constant.au2cm}cm^-1"
)
logger.info(f"J:{j_value*constant.au2ev}ev, {j_value*constant.au2cm}cm^-1")
j_matrix = np.diag(np.ones(nmols - 1) * j_value, k=-1)
j_matrix += j_matrix.T
ph_phys_dim = 5
omega = [Quantity(omega_value), Quantity(omega_value)]
D = [Quantity(0.), Quantity(D_value)]
ph = Phonon(omega, D, ph_phys_dim)
model = HolsteinModel(
[Mol(Quantity(elocalex), [ph], dipole_abs)] * nmols,
j_matrix,
)
# periodic nearest-neighbour interaction
mpo = Mpo(model)
periodic = Mpo.intersite(model, {
0: r"a^\dagger",
nmols - 1: "a"
}, {}, Quantity(j_value))
mpo = mpo.add(periodic).add(periodic.conj_trans())
# -*- coding: utf-8 -*-
import numpy as np
from renormalizer.model import Phonon, Mol, HolsteinModel
from renormalizer.utils import Quantity
from renormalizer.transport import EDGE_THRESHOLD
mol_num = 13
ph_list = [
Phonon.simple_phonon(Quantity(omega, "cm^{-1}"), Quantity(displacement, "a.u."), 4)
for omega, displacement in [[1e-10, 1e-10]]
]
j_constant = Quantity(0.8, "eV")
band_limit_model = HolsteinModel([Mol(Quantity(0), ph_list)] * mol_num, j_constant, 3)
# the temperature should be compatible with the low vibration frequency in TestBandLimitFiniteT
# otherwise underflow happens in exact propagator
low_t = Quantity(1e-7, "K")
def get_analytical_r_square(time_series: np.ndarray):
return 2 * (j_constant.as_au()) ** 2 * time_series ** 2
def assert_band_limit(ct, rtol):
analytical_r_square = get_analytical_r_square(ct.evolve_times_array)
# has evolved to the edge but not too large
assert EDGE_THRESHOLD < ct.latest_mps.e_occupations[0] < 0.1
# value OK
assert np.allclose(analytical_r_square, ct.r_square_array, rtol=rtol)
# -*- coding: utf-8 -*- import numpy as np from renormalizer.mps import Mpo from renormalizer.model import Phonon, Mol, MolList from renormalizer.utils import Quantity from renormalizer.utils.qutip_utils import get_clist, get_blist, get_hamiltonian, get_gs OMEGA = 1 DISPLACEMENT = 1 N_LEVELS = 2 N_SITES = 3 J = 1 ph = Phonon.simple_phonon(Quantity(OMEGA), Quantity(DISPLACEMENT), N_LEVELS) mol = Mol(Quantity(0), [ph]) mol_list = MolList([mol] * N_SITES, Quantity(J), 3) qutip_clist = get_clist(N_SITES, N_LEVELS) qutip_blist = get_blist(N_SITES, N_LEVELS) G = np.sqrt(DISPLACEMENT**2 * OMEGA / 2) qutip_h = get_hamiltonian(N_SITES, J, OMEGA, G, qutip_clist, qutip_blist) qutip_gs = get_gs(N_SITES, N_LEVELS)
