def composition_eq_point_curve():
cwd_1 = os.getcwd()
labels = {
0: 'Cs(Pb$_{x}$Sn$_{1-x})$Cl$_3$',
1: 'Cs(Pb$_{x}$Sn$_{1-x})$Br$_3$',
2: 'Cs(Pb$_{x}$Sn$_{1-x})$I$_3$'
}
colors = {
0: '#000000', # hex code for black
1: '#dd0000', # hex code for electric red
2: '#ffce00' # hex code for tangerine yellow
}
for counter, X in enumerate(['Cl', 'Br', 'I']):
data_dict = {}
for dir in [
r for r in glob.glob(os.getcwd() + "/*" + str(X) + "*")
if '.pdf' not in r
]:
os.chdir(dir)
print(dir)
cwd_2 = os.getcwd()
data = []
directories = glob.glob(os.getcwd() + "/disp_111_*_0_05")
if 'CsSnBr3_cubic' in dir:
directories = glob.glob(os.getcwd() + "/disp_Sn_111_*_0_05")
for sub_dir in directories:
os.chdir(sub_dir)
print(sub_dir)
energy = VaspReader(input_location='./OSZICAR'
).get_free_energies_from_oszicar()[-1]
crystal = VaspReader(input_location='./POSCAR').read_POSCAR()
energy = energy / crystal.total_num_atoms()
info = pickle.load(open('info.p', 'rb'))
info['energy'] = energy
data.append(info)
os.chdir(cwd_2)
os.chdir(cwd_1)
displacements = list(sorted([d['displacement'] for d in data]))
energy = []
for _dis in displacements:
for d in data:
if d['displacement'] == _dis:
energy.append(d['energy'])
energy = [e - energy[0] for e in energy]
x = np.array(displacements)
y = np.array(energy)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(pes, x, y)
x = [0 + (0.6 / 100) * i for i in range(100)]
y = pes(np.array(x), *popt)
a = popt[0]
b = popt[1]
if 2 * a / (4 * b) > 0:
min_x = 0
else:
min_x = math.sqrt(-2 * a / (4 * b))
concentration = data[0]['concentration']
data_dict[1.0 - concentration] = min_x
os.chdir(cwd_1)
x = list(sorted(data_dict.keys()))
plt.plot(x, [data_dict[_x] for _x in x],
'o-',
c=colors[counter],
label=labels[counter],
ms=12,
lw=3)
plt.legend()
plt.xlabel('$x$ in Cs(Pb$_{x}$Sn$_{1-x}$)X$_{3}$')
plt.ylabel('Minima on PES ($x_{\min}$, \AA)')
plt.tight_layout()
plt.savefig('xeq_comp_summary.pdf')
def pressure_eq_point_curve():
cwd = os.getcwd()
labels = {
0: 'CsPbBr$_3$',
1: 'Cs(Pb$_{0.5}$Sn$_{0.5})$Br$_3$',
2: 'CsSnBr$_3$'
}
colors = {
0: '#000000', # hex code for black
1: '#dd0000', # hex code for electric red
2: '#ffce00' # hex code for tangerine yellow
}
for counter, sys in enumerate([
'CsPbBr3_cubic', 'mixed_CsPbSnBr3_SC_1_1_1_CsPbSnBr3_5_str_17',
'CsSnBr3_cubic'
]):
os.chdir(sys)
this_dir = os.getcwd()
data = []
for dir in glob.glob("./disp_*"):
os.chdir(dir)
try:
energy = VaspReader(input_location='./OSZICAR'
).get_free_energies_from_oszicar()[-1]
crystal = VaspReader(input_location='./POSCAR').read_POSCAR()
energy = energy / crystal.total_num_atoms()
info = pickle.load(open('info.p', 'rb'))
info['energy'] = energy
data.append(info)
except:
pass
os.chdir(this_dir)
deformations = set([d['deformation'] for d in data])
deformations = list(sorted(deformations))
min_x = []
for id, _def in enumerate(deformations):
displacement = [
d['displacement'] for d in data if d['deformation'] == _def
]
displacement = list(sorted(displacement))
energy = []
for dis in displacement:
for d in data:
if (d['deformation'] == _def) and (d['displacement']
== dis):
energy.append(d['energy'])
energy = [e - energy[0] for e in energy]
x = np.array(displacement)
y = np.array(energy)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(pes, x, y)
a = popt[0]
b = popt[1]
if 2 * a / (4 * b) > 0:
min_x.append(0)
else:
min_x.append(math.sqrt(-2 * a / (4 * b)))
plt.plot(deformations,
min_x,
'o--',
label=labels[counter],
ms=10,
c=colors[counter])
_deformations = []
_min_x = []
for t in range(len(min_x)):
if (min_x[t] > 0):
_deformations.append(deformations[t])
_min_x.append(min_x[t])
x = np.array(_deformations)
y = np.array(_min_x)
from scipy import interpolate
f = interpolate.interp1d(x, y)
x = np.array([
min(_deformations) + k *
(max(_deformations) - min(_deformations)) / 100 for k in range(100)
])
y = f(x)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(square, y, x, maxfev=2000)
a = popt[0]
b = popt[1]
_y = np.array([(max(y) + 0.1 * max(y)) * k / 100 for k in range(100)])
_x = square(_y, *popt)
print(_x[0])
plt.plot(_x, _y, '-', lw=2, c=colors[counter])
os.chdir(cwd)
plt.xlim([0.015, 0.052])
plt.xlabel('Lattice Deformation $\delta $')
plt.ylabel('Minima on PES ($x_{\min}$, \AA)')
plt.legend()
plt.tight_layout()
plt.savefig("xeq_pressure_summary_Br.pdf")
def get_this_pes(directory=os.getcwd()):
cmap = matplotlib.cm.get_cmap('YlOrRd')
cwd = os.getcwd()
data = []
for dir in glob.glob(directory + "/disp_*"):
os.chdir(dir)
try:
energy = VaspReader(input_location='./OSZICAR'
).get_free_energies_from_oszicar()[-1]
crystal = VaspReader(input_location='./POSCAR').read_POSCAR()
energy = energy / crystal.total_num_atoms()
info = pickle.load(open('info.p', 'rb'))
info['energy'] = energy
data.append(info)
except:
pass
os.chdir(cwd)
deformations = set([d['deformation'] for d in data])
deformations = list(sorted(deformations))
max_def = max(deformations) + 0.0001
max_displacement = 0
min_x = []
min_y = []
for id, _def in enumerate(deformations):
displacement = [
d['displacement'] for d in data if d['deformation'] == _def
]
displacement = list(sorted(displacement))
energy = []
for dis in displacement:
for d in data:
if (d['deformation'] == _def) and (d['displacement'] == dis):
energy.append(d['energy'])
energy = [e - energy[0] for e in energy]
x = np.array(displacement)
y = np.array(energy)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(pes, x, y)
x = [0 + (0.6 / 100) * i for i in range(100)]
y = pes(np.array(x), *popt)
a = popt[0]
b = popt[1]
if 2 * a / (4 * b) > 0:
min_x.append(0)
min_y.append(0)
else:
min_x.append(math.sqrt(-2 * a / (4 * b)))
min_y.append(pes(math.sqrt(-2 * a / (4 * b)), *popt))
if id % 2 == 0:
plt.plot(x,
y,
'-',
c=cmap((0.05 + _def) / (2 * max_def)),
label=str(_def * 100) + '\%',
lw=2)
else:
plt.plot(x, y, '-', c=cmap((0.05 + _def) / (2 * max_def)), lw=2)
max_displacement = max(x)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.plot([0, max_displacement], [0, 0], 'k--')
plt.plot(min_x, min_y, 'o-', c='k', lw=3.5)
plt.legend()
plt.ylim([-0.01, 0.0150])
plt.xlim([0, max_displacement])
plt.xlabel('B-Cation Displacement $\Delta d$ (\AA)')
plt.ylabel('$E-E_{\Delta d=0}$ (eV/atom)')
plt.tight_layout()
plt.savefig('pes.pdf')
def get_pes_across_composition(X='Cl'):
cmap = matplotlib.cm.get_cmap('coolwarm')
cwd_1 = os.getcwd()
data_dict = {}
min_x = []
min_y = []
for dir in [
r for r in glob.glob(os.getcwd() + "/*" + str(X) + "*")
if '.pdf' not in r
]:
os.chdir(dir)
cwd_2 = os.getcwd()
data = []
for sub_dir in glob.glob(os.getcwd() + "/disp_111_*_0_05"):
os.chdir(sub_dir)
print(sub_dir)
energy = VaspReader(input_location='./OSZICAR'
).get_free_energies_from_oszicar()[-1]
crystal = VaspReader(input_location='./POSCAR').read_POSCAR()
energy = energy / crystal.total_num_atoms()
info = pickle.load(open('info.p', 'rb'))
info['energy'] = energy
data.append(info)
os.chdir(cwd_2)
os.chdir(cwd_1)
displacements = list(sorted([d['displacement'] for d in data]))
energy = []
for _dis in displacements:
for d in data:
if d['displacement'] == _dis:
energy.append(d['energy'])
energy = [e - energy[0] for e in energy]
x = np.array(displacements)
y = np.array(energy)
from scipy.optimize import curve_fit
popt, pcov = curve_fit(pes, x, y)
x = [0 + (0.6 / 100) * i for i in range(100)]
y = pes(np.array(x), *popt)
a = popt[0]
b = popt[1]
if 2 * a / (4 * b) > 0:
min_x.append(0)
min_y.append(0)
else:
min_x.append(math.sqrt(-2 * a / (4 * b)))
min_y.append(pes(math.sqrt(-2 * a / (4 * b)), *popt))
data_dict[1.0 - data[0]['concentration']] = {'x': x, 'y': y}
for k in list(sorted(data_dict.keys())):
plt.plot(data_dict[k]['x'],
data_dict[k]['y'],
'-',
c=cmap(k),
label=str(k))
# plt.legend(loc=2)
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.plot(min_x, min_y, 'o', c='#00743F')
max_displacemnt = 0.6
plt.xlim([0, max_displacemnt])
plt.plot([0, max_displacemnt], [0, 0], 'k--')
plt.xlabel('B-Cation Displacement $\Delta d$ (\AA)')
plt.ylabel('$E-E_{\Delta d=0}$ (eV/atom)')
plt.tight_layout()
plt.savefig(X + "_B_111_landscape.pdf")