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_offset(scheme):
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m = Mol(Quantity(0), [ph] * 2)
mlist = HolsteinModel(
[m] * 2,
Quantity(17),
)
mpo1 = Mpo(mlist)
assert mpo1.is_hermitian()
f1 = mpo1.full_operator()
evals1, _ = np.linalg.eigh(f1)
offset = Quantity(0.123)
mpo2 = Mpo(mlist, offset=offset)
f2 = mpo2.full_operator()
evals2, _ = np.linalg.eigh(f2)
assert np.allclose(evals1 - offset.as_au(), evals2)
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 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_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)
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())
@pytest.mark.parametrize("periodic", (True, False))
def test_thermal_equilibrium(periodic):
if periodic:
# -*- 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.model import Phonon, Mol, HolsteinModel
from renormalizer.utils import Quantity
from renormalizer.utils.qutip_utils import get_clist, get_blist, get_holstein_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])
model = HolsteinModel([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_holstein_hamiltonian(N_SITES, J, OMEGA, G, qutip_clist,
qutip_blist)
qutip_gs = get_gs(N_SITES, N_LEVELS)
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
model = HolsteinModel(
list(np.array(mlist)[mol_arangement]),
j_matrix_au[mol_arangement][:, mol_arangement],
)
evolve_dt = 160
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps, guess_dt=evolve_dt)
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=32)
ct = ChargeDiffusionDynamics(model,
evolve_config=evolve_config,
compress_config=compress_config,
init_electron=InitElectron.fc)
ct.dump_dir = "./"
ct.job_name = 'fmo'
ct.stop_at_edge = False
ct.evolve(evolve_dt=evolve_dt, evolve_time=40000)
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)]
holstein_model = HolsteinModel(
[Mol(elocalex, ph_list, dipole_abs)] * nmols,
_j_matrix,
)
holstein_model4 = holstein_model.switch_scheme(4)
offset = Quantity(2.28614053, "ev") + Quantity(holstein_model.gs_zpe)
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: