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)