def run_nbra_namd_wrapper(hvib, istate, outfile_name, params):
"""
A small wrapper function to run the nbra namd dynamics.
hvib ( list of list of matrices ): The vibronic hamiltonian for all timesteps
istate ( int ): Index of the initial state. This index is from 0
outfile ( string ): The name of the output file
params ( dict ): A dictionary of important dynamical parameters
returns: A list of energy values that is the electronic energy vs time. Returned energy is in eV
"""
print("decoherence_method = ", params["decoherence_method"])
params["outfile"] = outfile_name
params["nstates"] = hvib[0].num_of_rows
params["istate"] = istate
res_nbra_namd = step4.run([hvib], params)
nbra_namd = data_conv.MATRIX2nparray(res_nbra_namd)
# For SH energies
energy_nbra_namd = (nbra_namd[:, 3 * params["nstates"] + 2] -
nbra_namd[:, 1]) * units.au2ev
return energy_nbra_namd
"Boltz_opt"] = 1 # options: 0 (no), 1 (Pyxaid), 2 (Classical), 3 (N-state Boltzmann)
# Set initial Electronic states
params["istate"] = j # indexing here begins from 0, the last is 16
# Set initial Times
init_times = []
for k in xrange(20):
init_times.append(k * 50 + 1000)
params[
"init_times"] = init_times # starting points for sub-trajectories
# Set output file
params["outfile"] = "_out_" + str(
params["decoherence_method"]) + "_" + str(
params["istate"]) + ".txt" # output file
# For running NA-MD
# Hvib = step4.get_Hvib2(params) # get the Hivib for all data stes. Hvib is a list of lists
# Compute avergae band gap:
# avg_gap = decoherence_times.energy_gaps_av(Hvib, params["init_times"], params["nsteps"])
# Apply rigid energy shift to data
# data_con.scissor(Hvib, 1, shift)
# Optional: Print decoherence times
# Run NA-MD
res = step4.run(Hvib, params)
params["nfiles"] = 30
# Normal example
params["data_set_paths"] = [
"/mnt/c/cygwin/home/Alexey-user/Programming/Project_libra/tests/example_workflows/pyxaid2/example_1_Si/step3/res_setup1/"
]
# Pretend we have 2 directories:
#params["data_set_paths"].append("/mnt/c/cygwin/home/Alexey-user/Programming/Project_libra/tests/example_workflows/pyxaid2/example_1_Si/step3/res_setup1/")
params["Hvib_re_prefix"] = "Hvib_"
params["Hvib_re_suffix"] = "_re"
params["Hvib_im_prefix"] = "Hvib_"
params["Hvib_im_suffix"] = "_im"
# General simulaiton parameters
params["T"] = 300.0 # Temperature, K
params["ntraj"] = 10 # how many stochastic trajectories
params["sh_method"] = 1 # 0 - MSSH, 1 - FSSH
params[
"decoherence_method"] = 0 # 0 - no decoherence, 1 - decoherence (ID-A), 2 - MSDM, 3 - DISH
params["dt"] = 41.3413 # Nuclear dynamics integration timestep, in a.u.
params["nsteps"] = 10 # The length of the NA-MD trajectory
params["Boltz_opt"] = 3 # Option for the frustrated hops acceptance/rejection
params[
"istate"] = 3 # The index of the starting excited state (indexing from 0)
params["init_times"] = [0, 15] # starting points for sub-trajectories
params["outfile"] = "_out.txt" # output file
step4.run(params)
def myfunc(subtraj):
start_time = subtraj * subtraj_increment
print("\nsubtraj ", subtraj)
print("start time = ", start_time)
print("end time = ", start_time + subtraj_len)
md_time = [i for i in range(subtraj_len)]
md_time = np.array(md_time) * params["dt"] * units.au2fs
# Obtain the subtrajectory
for i in range(start_time, start_time + subtraj_len):
hvib_mb_subtrajs[subtraj].append(hvib_mb[0][i])
hvib_sd_subtrajs[subtraj].append(hvib_sd[0][i])
# Compute mb energy gaps and decoherence times
params["nsteps"] = subtraj_len
params["init_times"] = [0]
tau_mb, rates_mb = decoherence_times.decoherence_times(
hvib_mb_subtrajs[subtraj])
dE_mb = decoherence_times.energy_gaps(hvib_mb_subtrajs[subtraj])
avg_deco_mb = tau_mb * units.au2fs
print("Finished gather data for MD time",
params["init_times"][0] + params["nsteps"], "steps.")
print("Dephasing time between mb states 0 and 1 is:",
tau_mb.get(0, 1) * units.au2fs, "fs")
# Compute sd energy gaps and decoherence times
params["nsteps"] = subtraj_len
params["init_times"] = [0]
tau_sd, rates_sd = decoherence_times.decoherence_times(
hvib_sd_subtrajs[subtraj])
dE_sd = decoherence_times.energy_gaps(hvib_sd_subtrajs[subtraj])
avg_deco_sd = tau_sd * units.au2fs
print("Finished gather data for MD time",
params["init_times"][0] + params["nsteps"], "steps.")
print("Dephasing time between sd states 0 and 1 is:",
tau_sd.get(0, 1) * units.au2fs, "fs")
# Compute mb energies
mb_subtraj_energy = []
params["nstates"] = 31
for mb_index in range(params["nstates"]):
mb_subtraj_energy.append([])
for step in range(params["nsteps"]):
mb_subtraj_energy[mb_index].append(
hvib_mb_subtrajs[subtraj][step].get(mb_index, mb_index).real -
hvib_mb_subtrajs[subtraj][step].get(0, 0).real)
mb_subtraj_energy = np.array(mb_subtraj_energy)
tmp_mb_energies.append(mb_subtraj_energy)
# Compute sd energies
params["nstates"] = 64
sd_subtraj_energy = []
for sd_index in range(params["nstates"]):
sd_subtraj_energy.append([])
for step in range(params["nsteps"]):
sd_subtraj_energy[sd_index].append(
hvib_sd_subtrajs[subtraj][step].get(sd_index, sd_index).real -
hvib_sd_subtrajs[subtraj][step].get(0, 0).real)
sd_subtraj_energy = np.array(sd_subtraj_energy)
tmp_sd_energies.append(sd_subtraj_energy)
###############################################
###############################################
# Now it is time to run the NAMD
params["T"] = 300.0
params["ntraj"] = 250
params["sh_method"] = 1
params["Boltz_opt"] = 1
params["nsteps"] = subtraj_len
params["init_times"] = [0]
istates = [14, 18]
for istate in istates:
params["istate"] = istate # Recall index from 0
### FSSH
params["decoherence_method"] = 0
start = time.time()
# mb fssh
params["outfile"] = "_out_mb_fssh_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_fssh = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_fssh)
tmp_fssh_mb.append(mb_nbra_namd)
mb_hot_energy_fssh = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sys.exit(0)
#sd fssh
params["outfile"] = "_out_sd_fssh_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_fssh = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_fssh)
tmp_fssh_sd.append(sd_nbra_namd)
sd_hot_energy_fssh = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
### IDA
params["decoherence_method"] = 1
start = time.time()
# mb ida
params["outfile"] = "_out_mb_ida_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_ida = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_ida)
tmp_ida_mb.append(mb_nbra_namd)
mb_hot_energy_ida = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sd ida
params["outfile"] = "_out_sd_ida_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_ida = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_ida)
tmp_ida_sd.append(sd_nbra_namd)
sd_hot_energy_ida = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
### mSDM
params["decoherence_method"] = 2
start = time.time()
# mb msdm
params["outfile"] = "_out_mb_msdm_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 31 # total number of mb electronic states
res_mb_msdm = step4.run([hvib_mb_subtrajs[subtraj]], params)
mb_nbra_namd = data_conv.MATRIX2nparray(res_mb_msdm)
tmp_msdm_mb.append(mb_nbra_namd)
mb_hot_energy_msdm = (mb_nbra_namd[:, 95] -
mb_nbra_namd[:, 1]) * units.au2ev
#sd msdm
params["outfile"] = "_out_sd_msdm_subtraj_" + str(
subtraj) + "_istate" + str(istate) + ".txt"
params["nstates"] = 64 # total number of sd electronic states
res_sd_msdm = step4.run([hvib_sd_subtrajs[subtraj]], params)
sd_nbra_namd = data_conv.MATRIX2nparray(res_sd_msdm)
tmp_msdm_sd.append(sd_nbra_namd)
sd_hot_energy_msdm = (sd_nbra_namd[:, 194] -
sd_nbra_namd[:, 1]) * units.au2ev
end = time.time()
print("Time to run NBRA NAMD = ", end - start)
plt.figure(num=None,
figsize=(3.21, 2.41),
dpi=300,
edgecolor='black',
frameon=True)
plt.subplot(1, 1, 1)
plt.title('(CdSe)$_{33}$ MB traj' + str(subtraj) + ' istate' +
str(istate) + '',
fontsize=10)
plt.xlabel('Time, fs', fontsize=10)
plt.ylabel('Energy, eV', fontsize=10)
plt.ylim(0, 3.5)
plt.yticks([0, 1, 2, 3])
for sd_index in range(31):
plt.plot(md_time,
mb_subtraj_energy[sd_index] * units.au2ev,
label="",
linewidth=1,
color="gray")
plt.plot(md_time,
mb_hot_energy_fssh,
linewidth=2.5,
color="red",
label="FSSH")
plt.plot(md_time,
mb_hot_energy_ida,
linewidth=2.5,
color="green",
label="IDA")
plt.plot(md_time,
mb_hot_energy_msdm,
linewidth=2.5,
color="blue",
label="mSDM")
plt.tight_layout()
plt.legend(fontsize=8, ncol=3, loc="lower left")
plt.savefig("CdSe33_MB_Dyn_" + str(subtraj) + "_" + str(istate) +
".png",
dpi=300)
plt.figure(num=None,
figsize=(3.21, 2.41),
dpi=300,
edgecolor='black',
frameon=True)
plt.subplot(1, 1, 1)
plt.title('(CdSe)$_{33}$ SP traj' + str(subtraj) + ' istate' +
str(istate) + '',
fontsize=10)
plt.xlabel('Time, fs', fontsize=10)
plt.ylabel('Energy, eV', fontsize=10)
plt.ylim(0, 3.5)
plt.yticks([0, 1, 2, 3])
for sd_index in range(64):
plt.plot(md_time,
sd_subtraj_energy[sd_index] * units.au2ev,
label="",
linewidth=1,
color="gray")
plt.plot(md_time,
sd_hot_energy_fssh,
linewidth=2.5,
color="red",
label="FSSH")
plt.plot(md_time,
sd_hot_energy_ida,
linewidth=2.5,
color="green",
label="IDA")
plt.plot(md_time,
sd_hot_energy_msdm,
linewidth=2.5,
color="blue",
label="mSDM")
plt.tight_layout()
plt.legend(fontsize=8, ncol=3, loc="lower left")
plt.savefig("CdSe33_" + str(subtraj) + "_SP_Dyn_" + str(istate) +
".png",
dpi=300)