def generate_calculation_vasp(self,
structure,
parameters,
type='optimize',
pressure=0.0):
# import pymatgen as mg
from pymatgen.io import vasp as vaspio
ParameterData = DataFactory('parameter')
code = Code.get_from_string(parameters['code'])
# Set calculation
calc = code.new_calc(max_wallclock_seconds=3600,
resources=parameters['resources'])
calc.set_withmpi(True)
calc.label = 'VASP'
# POSCAR
calc.use_structure(structure)
# INCAR
incar = parameters['parameters']
if type == 'optimize':
vasp_input_optimize = dict(incar)
vasp_input_optimize.update({
'PREC': 'Accurate',
'ISTART': 0,
'IBRION': 2,
'ISIF': 3,
'NSW': 100,
'LWAVE': '.FALSE.',
'LCHARG': '.FALSE.',
'EDIFF': 1e-08,
'EDIFFG': -1e-08,
'ADDGRID': '.TRUE.',
'LREAL': '.FALSE.',
'PSTRESS': pressure
}) # unit: kb -> kB
incar = vasp_input_optimize
if type == 'optimize_constant_volume':
vasp_input_optimize = dict(incar)
vasp_input_optimize.update({
'PREC': 'Accurate',
'ISTART': 0,
'IBRION': 2,
'ISIF': 4,
'NSW': 100,
'LWAVE': '.FALSE.',
'LCHARG': '.FALSE.',
'EDIFF': 1e-08,
'EDIFFG': -1e-08,
'ADDGRID': '.TRUE.',
'LREAL': '.FALSE.'
})
incar = vasp_input_optimize
if type == 'forces':
vasp_input_forces = dict(incar)
vasp_input_forces.update({
'PREC': 'Accurate',
'ISYM': 0,
'ISTART': 0,
'IBRION': -1,
'NSW': 1,
'LWAVE': '.FALSE.',
'LCHARG': '.FALSE.',
'EDIFF': 1e-08,
'ADDGRID': '.TRUE.',
'LREAL': '.FALSE.'
})
incar = vasp_input_forces
if type == 'born_charges':
vasp_input_epsilon = dict(incar)
vasp_input_epsilon.update({
'PREC': 'Accurate',
'LEPSILON': '.TRUE.',
'ISTART': 0,
'IBRION': 1,
'NSW': 0,
'LWAVE': '.FALSE.',
'LCHARG': '.FALSE.',
'EDIFF': 1e-08,
'ADDGRID': '.TRUE.',
'LREAL': '.FALSE.'
})
incar = vasp_input_epsilon
# KPOINTS
kpoints = parameters['kpoints']
if 'kpoints_per_atom' in kpoints:
kpoints = vaspio.Kpoints.automatic_density(
structure.get_pymatgen_structure(),
kpoints['kpoints_per_atom'])
# num_kpoints = np.product(kpoints.kpts)
# if num_kpoints < 4:
# incar['ISMEAR'] = 0
# incar['SIGMA'] = 0.05
else:
if not 'style' in kpoints:
kpoints['style'] = 'Monkhorst'
# supported_modes = Enum(("Gamma", "Monkhorst", "Automatic", "Line_mode", "Cartesian", "Reciprocal"))
kpoints = vaspio.Kpoints(comment='aiida generated',
style=kpoints['style'],
kpts=(kpoints['points'], ),
kpts_shift=kpoints['shift'])
calc.use_kpoints(ParameterData(dict=kpoints.as_dict()))
# update incar (just in case something changed with kpoints)
incar = vaspio.Incar(incar)
calc.use_incar(ParameterData(dict=incar.as_dict()))
# POTCAR
pseudo = parameters['pseudo']
potcar = vaspio.Potcar(symbols=pseudo['symbols'],
functional=pseudo['functional'])
calc.use_potcar(ParameterData(dict=potcar.as_dict()))
# Parser settings
settings = {'PARSER_INSTRUCTIONS': []}
pinstr = settings['PARSER_INSTRUCTIONS']
pinstr.append({
'instr': 'array_data_parser',
'type': 'data',
'params': {}
})
pinstr.append({
'instr': 'output_parameters',
'type': 'data',
'params': {}
})
# additional files to return
settings.setdefault('ADDITIONAL_RETRIEVE_LIST', [
'vasprun.xml',
])
calc.use_settings(ParameterData(dict=settings))
calc.store_all()
return calc
'ALGO': 38,
'ISMEAR': 0,
'SIGMA': 0.01,
'GGA': 'PS'
}
es_settings = ParameterData(dict=incar_dict)
from pymatgen.io import vasp as vaspio
#kpoints
#kpoints_pg = vaspio.Kpoints.monkhorst_automatic(
# kpts=[2, 2, 2],
# shift=[0.0, 0.0, 0.0])
#kpoints = ParameterData(dict=kpoints_pg.as_dict())
potcar = vaspio.Potcar(symbols=['Ga', 'N'], functional='PBE')
settings_dict = {
'code': 'vasp541mpi@boston',
'parameters': incar_dict,
'kpoints_per_atom': 1000, # k-point density
'pseudos': potcar.as_dict()
}
# pseudos = ParameterData(dict=potcar.as_dict())
es_settings = ParameterData(dict=settings_dict)
# QE SPECIFIC
if False:
parameters_dict = {
'CONTROL': {
incar = vaspio.Incar(incar_dict)
calc.use_incar(ParameterData(dict=incar.as_dict()))
# KPOINTS
kpoints = kpoints_dict
kpoints = vaspio.Kpoints(comment='aiida generated',
style=kpoints['style'],
kpts=(kpoints['points'], ),
kpts_shift=kpoints['shift'])
calc.use_kpoints(ParameterData(dict=kpoints.as_dict()))
# POTCAR
pseudo = pseudo_dict
potcar = vaspio.Potcar(symbols=pseudo['symbols'],
functional=pseudo['functional'])
calc.use_potcar(ParameterData(dict=potcar.as_dict()))
# Define parsers to use
settings = {'PARSER_INSTRUCTIONS': []}
pinstr = settings['PARSER_INSTRUCTIONS']
pinstr += [{
'instr': 'array_data_parser',
'type': 'data',
'params': {}
}, {
'instr': 'output_parameters',
'type': 'data',
'params': {}
}, {
'instr': 'dummy_error_parser',
[0.5, 0.0, 0.5],
[0.5, 0.5, 0.0]
])
lattice = mg.Lattice(lattice)
struct = mg.Structure(
lattice,
[Mn, Mn, Ga],
# site coords
[[0.00, 0.00, 0.00], [0.25, 0.25, 0.25], [0.50, 0.50, 0.50]]
)
poscar = vaspio.Poscar(struct, comment='cubic Mn2Ga')
# POTCAR
# Note: for this to work Pymatgen needs to have an access to VASP pseudopotential directory
potcar = vaspio.Potcar(symbols=['Mn_pv', 'Mn_pv', 'Ga_d'], functional='PBE')
# KPOINTS
kpoints = vaspio.Kpoints.monkhorst_automatic(
kpts=(10, 10, 10), shift=(0.0, 0.0, 0.0)
)
# split the poscar for AiiDA serialization
poscar_parts = vplugin.disassemble_poscar(poscar)
# === Prepare Calculation
ParameterData = DataFactory('parameter')
StructureData = DataFactory('structure')
submit_test = True # CAUTION: changing this will affect your database