def calc_shellmax(atoms_in, EPI_beta):
atoms = copy.deepcopy(atoms_in)
nelem = atoms.get_nelem()
shellmax = (len(EPI_beta) - 1) / (nelem * (nelem - 1) / 2)
vf.confirm_int(shellmax)
shellmax = int(shellmax)
return shellmax
def write_poscar(a_pos, latt, lelem, pos, filename='POSCAR'):
latt = latt / a_pos
pos = pos / a_pos
temp = lelem.max()
vf.confirm_int(temp)
ns = np.zeros(int(temp))
for i in np.arange(len(ns)):
mask = lelem[:] == (i + 1)
temp = lelem[mask]
ns[i] = temp.shape[0]
mask = ns[:] != 0
ns = ns[mask]
f = open(filename, 'w+')
f.write('system name \n %22.16f \n' % (a_pos))
for i in np.arange(3):
f.write(' %22.16f %22.16f %22.16f \n' %
(latt[i, 0], latt[i, 1], latt[i, 2]))
for i in np.arange(len(ns)):
f.write(' %d ' % (ns[i]))
f.write('\nS \nC \n')
for i in np.arange(pos.shape[0]):
f.write(' %22.16f %22.16f %22.16f T T T \n' %
(pos[i, 0], pos[i, 1], pos[i, 2]))
f.close()
def vasp_read_post_param(filename='y_post_param', \
filename2='y_post_param_2', ):
ljobs = []
f = open(filename, 'r')
next(f)
for line in f:
temp = line.split()
job = {
'jobname': temp[0],
'ENCUT': temp[1][6:],
'ISMEAR': temp[2][7:],
'EDIFF': float(temp[3][6:]),
'ISIF': temp[4],
'EDIFFG': float(temp[5]),
'LREAL': temp[6][6:],
'KP': temp[7][3:],
}
ljobs.append(job)
f.close()
f = open(filename2, 'r')
next(f)
k = -1
for line in f:
temp = line.split()
k += 1
if temp[0] != ljobs[k]['jobname']:
sys.exit('ABORT: wrong post_param_2')
addgrid = float(temp[10]) / float(temp[9])
vf.confirm_int(addgrid)
addgrid = int(addgrid)
ljobs[k].update({
'ISTART': temp[1][7:],
'ICHARG': temp[2][7:],
'PREC': temp[3][5:],
'ISPIN': temp[4][6:],
'VOSKOWN': temp[5][8:],
'RWIGS': temp[6][6:],
'IALGO': temp[7][6:],
'ADDGRID': addgrid,
})
f.close()
return ljobs
def eval_Ef_from_EPI(atoms_in, EPI_beta):
from myvasp import vasp_EPI_dp_shell as vf2
atoms = copy.deepcopy(atoms_in)
nelem = atoms.get_nelem()
shellmax = (len(EPI_beta) - 1) / (nelem * (nelem - 1) / 2)
vf.confirm_int(shellmax)
shellmax = int(shellmax)
dp_shell = vf2.calc_pairs_per_shell(atoms,
shellmax=shellmax,
write_dp=False)
X = np.append(1.0, -0.5 * dp_shell)
Ef = np.dot(X, EPI_beta)
return Ef
def calc_natomsl_nlayers_nmiss(dz, dz_b):
dz = dz.copy()
dz_b = dz_b.copy()
# interplane id in dz
intp_id = np.array([])
for item in np.unique(dz_b):
temp, = np.where(dz == item)
intp_id = np.append(intp_id, temp)
intp_id.sort()
d_intp_id = np.diff(intp_id)
vf.confirm_int(d_intp_id)
# use the most frequent value as the natoms per layer
natomsl = np.bincount(d_intp_id.astype(int)).argmax()
# natomsl = calc_natomsl(d_intp_id)
vf.confirm_int(d_intp_id / natomsl)
natoms = len(dz) + 1
nlayers = natoms / natomsl
temp = np.ceil((intp_id[0] + 1) / natomsl)
nmiss = natomsl * temp - (intp_id[0] + 1)
vf.confirm_int([natomsl, nlayers, nmiss])
print('==> natomsl, nlayers, nmiss:', \
natomsl, nlayers, nmiss)
return natomsl, nlayers, nmiss
def calc_gamma_s(EPI_beta, pos_filename, a_fcc, b_slip):
atoms = vf.my_read_vasp( pos_filename )
latt = atoms.get_cell()[:]
natoms = atoms.get_positions().shape[0]
b = atoms.pos_a0/np.sqrt(2)
nx = np.around( latt[0,0]/b )
vf.confirm_int(nx)
nx = int(nx)
nlayers, nmiss = vf_shift.check_layers(filename = pos_filename)
vf.confirm_0(nmiss)
nz = nlayers
E_s = calc_E_s(atoms, nx, nz, EPI_beta, b_slip)
vf.confirm_0( E_s.shape - np.array([nz/6, 2]) )
qe = vf.phy_const('qe')
gamma_s = E_s/(natoms/nz) / (np.sqrt(3)/2*a_fcc**2/2) *qe*1e23
return gamma_s, nz
def plot_dp_shell(atoms_in,
EPI_beta=np.array([]),
dp_shell=np.array([]),
dp_type='dp'):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
from myvasp import vasp_EPI_dp_shell as vf2
atoms = copy.deepcopy(atoms_in)
nelem = atoms.get_nelem()
shellmax = (len(EPI_beta) - 1) / (nelem * (nelem - 1) / 2)
vf.confirm_int(shellmax)
shellmax = int(shellmax)
if len(dp_shell) < 0.1:
dp_shell = vf2.calc_pairs_per_shell(atoms, \
shellmax=shellmax, write_dp=True)
else:
print('==> Use input dp_shell.')
temp = int(len(dp_shell) / shellmax)
dp_shell_2 = dp_shell.reshape(shellmax, temp)
rcry, ncry = vf2.crystal_shell('fcc')
xi = rcry[0:shellmax].copy()
fig_xlim = np.array([xi[0] - 0.15, xi[-1] + 0.15])
# fig_ylim = np.array([-0.5, 0.5])
elem_sym = pd.unique(atoms.get_chemical_symbols())
print('elem_sym:', elem_sym)
nelem = elem_sym.shape[0]
fig_wh = [2.7, 2.5]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.27, 0.17, 0.70, 0.78])
ax1.set_position(fig_pos)
ax1.plot(fig_xlim, [0, 0], ':k')
k = -1
for i in np.arange(nelem):
for j in np.arange(i, nelem):
if i != j:
k = k + 1
elems_name = '%s%s' % (elem_sym[i], elem_sym[j])
# str1 = '$\\Delta \\eta_{\\mathrm{%s}, d}$' %(elems_name)
str1 = '%s' % (elems_name)
mycolor, mymarker = mycolors(elems_name)
ax1.plot(xi, dp_shell_2[:, k], '-', \
color = mycolor, marker = mymarker, \
label = str1)
vf.confirm_0(dp_shell_2.shape[1] - (k + 1))
ax1.legend(loc='best', ncol=2, framealpha=0.4, \
fontsize=7)
ax1.set_xlabel('Pair distance $d/a$')
ax1.set_xlim(fig_xlim)
# ax1.set_ylim( fig_ylim )
fig_ylim = ax1.get_ylim()
if dp_type == 'dp':
ax1.set_ylabel('$\\Delta \\eta_{nm, d}$')
if len(EPI_beta) > 0.1:
Ef = -0.5 * np.dot(dp_shell, EPI_beta[1:])
str1 = '$E_{f, \\mathrm{Pred}} - {E}^\\mathrm{rand}_{f}$\n= %.3f meV/atom' % (
Ef * 1e3)
ax1.text(
fig_xlim[0]+(fig_xlim[1]-fig_xlim[0])*0.95, \
fig_ylim[0]+(fig_ylim[1]-fig_ylim[0])*0.06, str1, \
horizontalalignment='right' )
filename = 'y_post_dp_shell.pdf'
elif dp_type == 'SRO':
ax1.set_ylabel('Warren-Cowley SRO $\\alpha_{nm, d}$')
filename = 'y_post_WC_SRO_shell.pdf'
plt.savefig(filename)
plt.close('all')
return dp_shell