calc.use_structure(structure)
calc.use_parameters(Dict(dict=dynaphopy_parameters))
calc.use_force_constants(force_constants)
calc.use_trajectory(trajectory)
calc.store_all()
calc.submit()
print("submitted calculation with PK={}".format(calc.dbnode.pk))
LammpsOptimizeCalculation = CalculationFactory('lammps.optimize')
inputs = LammpsOptimizeCalculation.get_builder()
# Computer options
options = AttributeDict()
options.account = ''
options.qos = ''
options.resources = {
'num_machines': 1,
'num_mpiprocs_per_machine': 1,
'parallel_env': 'localmpi',
'tot_num_mpiprocs': 1
}
#options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
}
# KPOINTS equivalent
# Set kpoint mesh
KMESH = [9, 9, 9]
# POTCAR equivalent
# Potential_family is chosen among the list given by
# 'verdi data vasp-potcar listfamilies'
POTENTIAL_FAMILY = 'pbe'
# The potential mapping selects which potential to use, here we use the standard
# for silicon, this could for instance be {'Si': 'Si_GW'} to use the GW ready
# potential instead
POTENTIAL_MAPPING = {'Si': 'Si'}
# Jobfile equivalent
# In options, we typically set scheduler options.
# See https://aiida.readthedocs.io/projects/aiida-core/en/latest/scheduler/index.html
# AttributeDict is just a special dictionary with the extra benefit that
# you can set and get the key contents with mydict.mykey, instead of mydict['mykey']
OPTIONS = AttributeDict()
OPTIONS.account = 'nn9995k'
OPTIONS.qos = ''
OPTIONS.resources = {'num_machines': 1, 'num_mpiprocs_per_machine': 16}
OPTIONS.queue_name = ''
OPTIONS.max_wallclock_seconds = 3600
OPTIONS.max_memory_kb = 1024000
main(CODE_STRING, INCAR, KMESH, STRUCTURE, POTENTIAL_FAMILY,
POTENTIAL_MAPPING, OPTIONS)
parameters_md = {
"timestep": 0.001,
"temperature": 300,
"thermostat_variable": 0.5,
"equilibrium_steps": 2000,
"total_steps": 2000,
"dump_rate": 1,
}
CombinateCalculation = CalculationFactory("lammps.force")
inputs = CombinateCalculation.get_builder()
# Computer options
options = AttributeDict()
options.account = ""
options.qos = ""
options.resources = {
"num_machines": 1,
"num_mpiprocs_per_machine": 1,
"parallel_env": "localmpi",
"tot_num_mpiprocs": 1,
}
# options.queue_name = 'iqtc04.q'
options.max_wallclock_seconds = 3600
inputs.metadata.options = options
# Setup code
inputs.code = Code.get_from_string(codename)
# setup nodes
}
# KPOINTS equivalent
# Set kpoint mesh
KMESH = [9, 9, 9]
# POTCAR equivalent
# Potential_family is chosen among the list given by
# 'verdi data vasp-potcar listfamilies'
POTENTIAL_FAMILY = 'pbe'
# The potential mapping selects which potential to use, here we use the standard
# for silicon, this could for instance be {'Si': 'Si_GW'} to use the GW ready
# potential instead
POTENTIAL_MAPPING = {'Si': 'Si'}
# Jobfile equivalent
# In options, we typically set scheduler options.
# See https://aiida.readthedocs.io/projects/aiida-core/en/latest/scheduler/index.html
# AttributeDict is just a special dictionary with the extra benefit that
# you can set and get the key contents with mydict.mykey, instead of mydict['mykey']
OPTIONS = AttributeDict()
OPTIONS.account = ''
OPTIONS.qos = ''
OPTIONS.resources = {'num_machines': 1, 'num_mpiprocs_per_machine': 1}
OPTIONS.queue_name = ''
OPTIONS.max_wallclock_seconds = 3600
OPTIONS.max_memory_kb = 1024000
main(CODE_STRING, INCAR, KMESH, STRUCTURE, POTENTIAL_FAMILY,
POTENTIAL_MAPPING, OPTIONS)
