def _test_get_entropy_TO(self):
"""
de を記録して entropy を算出する
"""
path = os.path.join(self.PATH, "AlCu/wien/TO")
fcc = FCCXtal.from_pickle_ecis(os.path.join(path, 'cluster.pickle'),
arrange='random', conc=0.25, size=30)
mc = MonteCarlo(fcc, T=10000)
mc.loop_fcc_micro_single(2000)
def _test_gs(self):
"""
基底状態探索
エネルギーのみを出力
"""
path = os.path.join(self.PATH, "AlCu/voldep/4.0/")
fcc = FCCXtal.from_pickle_ecis(os.path.join(path, 'cluster.pickle'),
arrange='random', conc=0.90, size=10)
fcc.from_pickle_cell(os.path.join(path, 'AlCu.pickle'))
# mc = MonteCarlo(fcc, T=500)
# mc.loop_fcc_micro_single(500)
# 90 300
mc = MonteCarlo(fcc, T=20)
mc.loop_fcc_micro_single(10000)
fcc.make_poscar(os.path.join(path, 'POSCAR'))
fcc.save_cell(os.path.join(path, 'AlCu'))
def EstimateDOS(system, eps_log_modification_factor, restart=None):
"""
イタレーションによって DOS を求める
"""
rand = numpy.random.mtrand.RandomState(100) # 乱数ジェネレーター
if not restart:
# 各エネルギーに対してインデックスを持つヒストグラムと状態密度
histgram = numpy.array([0 for i in range(system.number_of_energies)])
log_density_of_state = [0 for i in range(system.number_of_energies)]
# 修正ファクター: DOS を積み上げていくのに使用
log_modification_factor = 1
# モンテカルロステップカウンター 定期的にヒストグラムの平滑化具合をチェックするのに使用
current_energy = system.GetEnergy()
# エネルギー最小、最大の状態を保存する用意
minimum_energy = current_energy
maximum_energy = current_energy
minimum_e_cell = system.state.cell.copy()
maximum_e_cell = system.state.cell.copy()
else:
histgram = restart["histgram"]
log_density_of_state = restart["log_DOS"]
log_modification_factor = restart["log_modification_factor"]
system.state.cell = restart["current_state"]
current_energy = system.GetEnergy()
minimum_energy = restart["minimum_state"][0]
maximum_energy = restart["maximum_state"][0]
minimum_e_cell = restart["minimum_state"][1]
maximum_e_cell = restart["maximum_state"][1]
counter_mc = 0
def save_conditions():
"""状態を保存"""
conditions = {"minimum_state":[minimum_energy, minimum_e_cell],
"maximum_state":[maximum_energy, maximum_e_cell],
"current_state": system.state.cell,
"log_DOS": log_density_of_state,
"log_modification_factor": log_modification_factor,
"histgram": histgram}
fname = "work/save.pickle"
with open(fname, 'wb') as wbfile:
pickle.dump(conditions, wbfile)
num = 0
system.GoNextState()
while log_modification_factor > eps_log_modification_factor:
counter_mc += 1
#状態変化前後でのエネルギー値を取得
new_energy = system.GetEnergy()
mc = MonteCarlo(system.state, T=0)
try:
log_dos_new = log_density_of_state[new_energy[1]]
except IndexError:
save_conditions()
print("out of energy range !")
print(new_energy)
exit()
log_dos_current = log_density_of_state[current_energy[1]]
if rand.rand() < min(1.0, numpy.exp(log_dos_current - log_dos_new)):
log_density_of_state[new_energy[1]] += log_modification_factor
histgram[new_energy[1]] += 1
current_energy = new_energy
else:
system.BackPreviousState()
log_density_of_state[current_energy[1]] += log_modification_factor
histgram[current_energy[1]] += 1
system.GoNextState()
if current_energy < minimum_energy:
minimum_energy = current_energy
minimum_e_cell = system.state.cell.copy()
print('search_stable_state')
print(system.state.get_energy())
system.BackPreviousState()
mc.trans_prob = mc.trans_prob_stable
mc.loop_fcc_micro_single(5000)
system.state.energy = system.state.get_energy()
if current_energy > maximum_energy:
maximum_energy = current_energy
maximum_e_cell = system.state.cell.copy()
print('search_unstable_state')
print(current_energy)
print(system.state.get_energy())
system.BackPreviousState()
mc.trans_prob = mc.trans_prob_unstable
print(system.state.get_energy())
mc.loop_fcc_micro_single(5000)
system.state.energy = system.state.get_energy()
if counter_mc % 1000 == 0:
# print(minimum_energy, maximum_energy, log_modification_factor)
# print(histgram)
if counter_mc % 100000 == 0:
counter_mc = 0
save_conditions()
judge = IsFlat_with_gap(histgram, num)
if judge:
num = judge
histgram = numpy.array([0 for i in range(system.number_of_energies)])
log_modification_factor *= 0.5
# print('here')
# print(log_modification_factor)
save_conditions()
return log_density_of_state
