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))
plt.plot(density_change[1:], energy_relative, 'o')
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
if os.path.isfile('../properties.json'):
properties = rmd.fileload('../properties.json')
if 'ISPIN' not in incar:
if rmd.detect_is_mag(properties['mag']):
incar.update({'ISPIN': 2})
else:
incar.update({'ISPIN': 1})
# POTCAR dump
rmd.write_potcar(run_specs)
potcars = mg.io.vasp.Potcar.from_file('POTCAR')
incar['ENCUT'] = rmd.get_max_ENMAX(potcars)
NBANDS, basisfunctions_str = get_NBANDS_and_basisfunctions_str(potcars, structure)
incar['NBANDS'] = NBANDS
# KPOINTS
kpoints = rmd.read_kpoints(run_specs, structure)
# write input files and run vasp
incar.write_file('INCAR')
kpoints.write_file('KPOINTS')
structure.to(filename='POSCAR')
rmd.run_vasp()
# LOBSTER
shutil.copy(os.path.join(start_dir, 'INPUT/lobsterin'), 'lobsterin')
with open('lobsterin', 'a') as f:
f.write(basisfunctions_str)
call('lobster-2.0.0 | tee -a stdout', shell=True)
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