def write_data(crt):
# -- Save diffusivity and conductivity of current step
# -- Skip initial heating process and stablizing process : set as start_num
start_num = 1
if crt >= start_num:
vaspruns = []
for i in range(start_num, crt + 1):
dirname = "run%03d" % i
vasprun = dirname + "/vasprun.xml.gz"
vaspruns.append(vasprun)
# -- collect all smoothing modes of analyzer
analyzers = {}
for mode in [False, 'constant', 'max']:
try:
analyzers[mode] = DiffusionAnalyzer.from_files(vaspruns,
specie=specie,
smoothed=mode,
min_obs=60)
except:
analyzers[mode] = None
# -- save DiffusionAnalzyer as pickle to plot msd quickly
if crt % 10 == 0:
with open("analyzer%03d.pkl" % crt, 'wb') as save_data:
pickle.dump(analyzers, save_data)
os.system("gzip analyzer%03d.pkl" % crt)
# -- write data
f = open("data_%sK.csv" % temp, "a")
timestep = (crt - start_num + 1) * nsw * 2 / 1000
step_info = "%d,%d," % (crt, timestep)
f.write(step_info)
for mode in [False, 'constant', 'max']:
if analyzers[mode] == None:
diffusivity, conductivity = 0.0, 0.0
else:
sd = analyzers[mode].get_summary_dict()
diffusivity = sd['D']
conductivity = sd['S']
f = open("data_%sK.csv" % temp, "a")
line = "%.10f,%.4f," % (diffusivity, conductivity)
f.write(line)
f.write("\n")
f.close()
def Diffusion(self, skip=10, spaces='S', temp=[300, 310, 350, 390]):
from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer, fit_arrhenius, get_arrhenius_plot
import shutil
import os
os.chdir(self.dire)
filePath = self.dire
file = []
for dirpath, dirnames, filenames in os.walk(filePath):
# print(dirpath)
for name in dirnames:
path = os.path.join(filePath, name)
path1 = os.path.join(path, 'vasprun.xml')
print(path1)
file.append(str(path1))
diff = DiffusionAnalyzer.from_files(file,
specie=spaces,
step_skip=skip)
diff.get_msd_plot()
diff.get_summary_dict()
fit = fit_arrhenius(temps=temp, diffusivities=diff.diffusivity)
plot = get_arrhenius_plot(temps=temp, diffusivities=diff.diffusivity)
temperatures = [500, 600, 700, 800, 900, 1000]
tetrels = ["Ge", "Sn"]
for i in temperatures:
for j in tetrels:
x1 = Xdatcar(f'{i}K/{j}/1/XDATCAR')
x2 = Xdatcar(f'{i}K/{j}/2/XDATCAR')
x3 = Xdatcar(f'{i}K/{j}/3/XDATCAR')
x4 = Xdatcar(f'{i}K/{j}/4/XDATCAR')
x5 = Xdatcar(f'{i}K/{j}/5/XDATCAR')
structures = x1.structures + x2.structures + x3.structures + x4.structures + x5.structures
s500K = [
"500/Sn/1/vasprun.xml", "500/Sn/2/vasprun.xml", "500/Sn/3/vasprun.xml"
]
d500K = DiffusionAnalyzer.from_files(s500K, specie="Na", smoothed=max)
s600K = [
"600/Sn/1/vasprun.xml", "600/Sn/2/vasprun.xml", "600/Sn/3/vasprun.xml"
]
d600K = DiffusionAnalyzer.from_files(s600K, specie="Na", smoothed=max)
s700K = [
"700/Sn/1/vasprun.xml", "700/Sn/2/vasprun.xml", "700/Sn/3/vasprun.xml"
]
d700K = DiffusionAnalyzer.from_files(s700K, specie="Na", smoothed=max)
s800K = [
"800/Sn/1/vasprun.xml", "800/Sn/2/vasprun.xml", "800/Sn/3/vasprun.xml"
]
d800K = DiffusionAnalyzer.from_files(s800K, specie="Na", smoothed=max)
s900K = [
"900/Sn/1/vasprun.xml", "900/Sn/2/vasprun.xml", "900/Sn/3/vasprun.xml"
]
