for V, st in zip(volume, structures):
rmd.chdir(str(np.round(V, 2)))
if run_specs['phonopy']['mode'] == 'force_set':
structure = mg.Structure.from_dict(st)
structure.to(filename='POSCAR')
call('phonopy -d --dim="' + phonopy_specs['dim'] + '" > /dev/null', shell=True)
os.remove('SPOSCAR')
disp_structures = sorted(glob.glob('POSCAR-*'))
disp_dirs = ['disp-' + i.split('POSCAR-')[1] for i in disp_structures]
for disp_d, disp_p in zip(disp_dirs, disp_structures):
rmd.chdir(disp_d)
rmd.init_stdout()
shutil.move('../' + disp_p, 'POSCAR')
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
rmd.write_potcar(run_specs)
job = str(V) + '-' + disp_d
shutil.copy(cwd + '/INPUT/deploy.job', job)
call('sed -i "/python/c time ' + rmd.VASP_EXEC + ' 2>&1 | tee -a stdout" ' + job, shell=True)
call('M ' + job, shell=True)
os.remove(job)
os.chdir('..')
elif run_specs['phonopy']['mode'] == 'force_constant':
rmd.init_stdout()
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
structure = mg.Structure.from_dict(st)
structure.to(filename='POSCAR')
call('phonopy -d --dim="' + phonopy_specs['dim'] + '" > /dev/null', shell=True)
os.rename('POSCAR', 'POSCAR_orig')
os.rename('SPOSCAR', 'POSCAR')
def volume_fitting(structure, is_mag, fitting_results):
"""
Construct a loop to get and fit the energy with changing volumes, return
some indicators showing if the fit is "good" or not.
"""
volume = np.linspace(V_begin, V_end, V_sample_point_num)
energy = np.zeros(len(volume))
mag = np.zeros(len(volume))
structures = []
for i, V in enumerate(volume):
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
structure.scale_lattice(V)
structure.to(filename='POSCAR')
rmd.write_potcar(run_specs)
rmd.run_vasp()
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[i] = oszicar.final_energy
structure = mg.Structure.from_file('CONTCAR')
structures.append(structure.as_dict())
if is_mag:
mag[i] = oszicar.ionic_steps[-1]['mag']
# dump in case error in fitting
fitting_results.append({'volume': volume.tolist(), 'energy': energy.tolist(), 'mag': mag.tolist(), 'structures': structures})
rmd.filedump(fitting_results, 'fitting_results.json')
# plot in case error in fitting
plt.plot(volume, energy, 'o')
plt.tight_layout()
plt.savefig('eos_fit.pdf')
plt.close()
# fitting and dumping
fitting_result_raw = pv.fitting.eos_fit(volume, energy, plot=True)
plt.savefig('eos_fit.pdf')
plt.close()
params = fitting_result_raw['params']
fitting_results[-1]['pressure'] = pv.fitting.birch_murnaghan_p(volume,
params['V0'], params['B0'],
params['B0_prime']).tolist()
fitting_results[-1]['params'] = params
fitting_results[-1]['r_squared'] = fitting_result_raw['r_squared']
rmd.filedump(fitting_results, 'fitting_results.json')
# a simplifed version of the file dump
fitting_params = params.copy()
fitting_params['r_squared'] = fitting_result_raw['r_squared']
rmd.filedump(fitting_params, 'fitting_params.json')
# uncomment to make calculation faster by switching off ISPIN if possible
# is_mag = rmd.detect_is_mag(mag)
# if not is_mag:
# incar.update({'ISPIN': 1})
V0 = params['V0']
V0_ralative_pos = (V0 - V_begin) / (V_end - V_begin)
is_V0_within_valley = V0_ralative_pos > 0.4 and V0_ralative_pos < 0.6
is_range_proportional = (volume[-1] - volume[0])/V0 < 0.25
is_well_fitted = (is_V0_within_valley and is_range_proportional)
return is_well_fitted, V0, structure, is_mag
force_gamma = kpoints_specs['force_gamma']
else:
force_gamma = False
if 'density_change' in kpoints_specs:
density_change = np.array(kpoints_specs['density_change'])
else:
density_change = np.array(range(kpoints_specs['begin'], kpoints_specs['end'], kpoints_specs['step']))
energy = np.zeros(len(density_change))
for i, kp in enumerate(density_change):
incar.write_file('INCAR')
kpoints = mg.io.vasp.Kpoints.automatic_density(structure, kp, force_gamma=force_gamma)
kpoints.write_file('KPOINTS')
structure.to(filename='POSCAR')
rmd.write_potcar(run_specs)
rmd.run_vasp()
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[i] = oszicar.final_energy
energy /= structure.num_sites
plt.plot(density_change, energy, 'o')
ax = plt.gca()
plt.xlabel('KPPRA')
plt.ylabel('Energy (eV)')
plt.tight_layout()
plt.savefig('energy-kps.pdf')
plt.close()
np.savetxt('energy-kps.txt', np.column_stack((density_change, energy)), '%12.4f', header='kps energy')
energy_relative = np.abs(np.diff(energy))
def volume_fitting(structure, is_mag, fitting_results):
"""
Construct a loop to get and fit the energy with changing volumes, return
some indicators showing if the fit is "good" or not.
"""
volume = np.linspace(V_begin, V_end, V_sample_point_num)
energy = np.zeros(len(volume))
mag = np.zeros(len(volume))
structures = []
for i, V in enumerate(volume):
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
structure.scale_lattice(V)
structure.to(filename='POSCAR')
rmd.write_potcar(run_specs)
rmd.run_vasp()
oszicar = mg.io.vasp.Oszicar('OSZICAR')
energy[i] = oszicar.final_energy
structure = mg.Structure.from_file('CONTCAR')
structures.append(structure.as_dict())
if is_mag:
mag[i] = oszicar.ionic_steps[-1]['mag']
# dump in case error in fitting
fitting_results.append({
'volume': volume.tolist(),
'energy': energy.tolist(),
'mag': mag.tolist(),
'structures': structures
})
rmd.filedump(fitting_results, 'fitting_results.json')
# plot in case error in fitting
plt.plot(volume, energy, 'o')
plt.tight_layout()
plt.savefig('eos_fit.pdf')
plt.close()
# fitting and dumping
fitting_result_raw = pv.fitting.eos_fit(volume, energy, plot=True)
plt.savefig('eos_fit.pdf')
plt.close()
params = fitting_result_raw['params']
fitting_results[-1]['pressure'] = pv.fitting.birch_murnaghan_p(
volume, params['V0'], params['B0'], params['B0_prime']).tolist()
fitting_results[-1]['params'] = params
fitting_results[-1]['r_squared'] = fitting_result_raw['r_squared']
rmd.filedump(fitting_results, 'fitting_results.json')
# a simplifed version of the file dump
fitting_params = params.copy()
fitting_params['r_squared'] = fitting_result_raw['r_squared']
rmd.filedump(fitting_params, 'fitting_params.json')
# uncomment to make calculation faster by switching off ISPIN if possible
# is_mag = rmd.detect_is_mag(mag)
# if not is_mag:
# incar.update({'ISPIN': 1})
V0 = params['V0']
V0_ralative_pos = (V0 - V_begin) / (V_end - V_begin)
is_V0_within_valley = V0_ralative_pos > 0.4 and V0_ralative_pos < 0.6
is_range_proportional = (volume[-1] - volume[0]) / V0 < 0.25
is_well_fitted = (is_V0_within_valley and is_range_proportional)
return is_well_fitted, V0, structure, is_mag
