def get_custodian_task(spec): task_type = spec['task_type'] v_exe = 'VASP_EXE' # will be transformed to vasp executable on the node if 'optimize structure (2x)' in task_type: jobs = VaspJob.double_relaxation_run(v_exe, gzipped=False) handlers = [ VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler(), NonConvergingErrorHandler() ] else: jobs = [VaspJob(v_exe)] handlers = [ VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler() ] params = { 'jobs': [j_decorate(j.to_dict) for j in jobs], 'handlers': [h.to_dict for h in handlers], 'max_errors': 10 } return VaspCustodianTask(params)
def test_mesh_symmetry(self): h = MeshSymmetryErrorHandler("vasp.ibzkpt") h.check() d = h.correct() self.assertEqual(d["errors"], ['mesh_symmetry']) self.assertEqual(d["actions"], [{'action': {'_set': {'kpoints': [[4, 4, 4]]}}, 'dict': 'KPOINTS'}])
def test_mesh_symmetry(self): h = MeshSymmetryErrorHandler("vasp.ibzkpt") h.check() d = h.correct() self.assertEqual(d["errors"], ["mesh_symmetry"]) self.assertEqual( d["actions"], [{ "action": { "_set": { "kpoints": [[4, 4, 4]] } }, "dict": "KPOINTS" }], )
def get_custodian_task(spec): task_type = spec['task_type'] v_exe = 'VASP_EXE' # will be transformed to vasp executable on the node handlers = [ VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler(), NonConvergingErrorHandler(), PositiveEnergyErrorHandler() ] if 'optimize structure (2x)' in task_type: jobs = VaspJob.double_relaxation_run(v_exe) elif 'static' in task_type or 'deformed' in task_type: jobs = [VaspJob(v_exe)] else: # non-SCF runs jobs = [VaspJob(v_exe)] handlers = [] params = { 'jobs': [j_decorate(j.as_dict()) for j in jobs], 'handlers': [h.as_dict() for h in handlers], 'max_errors': 5 } return VaspCustodianTask(params)
def run_task(self, fw_spec): #workdir=fw_spec['workdir'] vasp_cmd = fw_spec['vasp_cmd'] #with cd(workdir): incar = Incar.from_file('INCAR') kpoints = Kpoints.from_file('KPOINTS') poscar = Poscar.from_file('POSCAR') potcar = Potcar.from_file('POTCAR') try: out = Outcar(work_dir + '/OUTCAR') if len(out.run_stats) != 7: raise VaspDoneError() except: try: contcar = Structure.from_file('CONTCAR') os.rename('CONTCAR', 'POSCAR') except: pass job = VaspJob(vasp_cmd) handlers=[VaspErrorHandler(),UnconvergedErrorHandler(),FrozenJobErrorHandler(),\ NonConvergingErrorHandler(nionic_steps=2, change_algo=True),MeshSymmetryErrorHandler()] c = Custodian(handlers, [job], max_errors=10) c.run() else: print 'Vasp job was already done well. No need to rerun!'
def run_task(self, fw_spec): workdir = fw_spec['workdir'] vasp_cmd = fw_spec['vasp_cmd'] os.chdir(workdir) jobs = VaspJob.double_relaxation_run(vasp_cmd) handlers=[VaspErrorHandler(),UnconvergedErrorHandler(),FrozenJobErrorHandler(),\ NonConvergingErrorHandler(nionic_steps=5, change_algo=True),MeshSymmetryErrorHandler()] c = Custodian(handlers, jobs, max_errors=10) c.run()
def run_task(self, fw_spec): handler_groups = { "default": [VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(),PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(), DriftErrorHandler()], "strict": [VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(),PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(), AliasingErrorHandler(), DriftErrorHandler()], "md": [VaspErrorHandler(), NonConvergingErrorHandler()], "no_handler": [] } vasp_cmd = env_chk(self["vasp_cmd"], fw_spec) if isinstance(vasp_cmd, six.string_types): vasp_cmd = os.path.expandvars(vasp_cmd) vasp_cmd = shlex.split(vasp_cmd) # initialize variables scratch_dir = env_chk(self.get("scratch_dir"), fw_spec) gzip_output = self.get("gzip_output", True) max_errors = self.get("max_errors", 5) auto_npar = env_chk(self.get("auto_npar"), fw_spec, strict=False, default=False) gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"), fw_spec, strict=False, default=None) jobs = [VaspJob(vasp_cmd, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd)] # construct handlers handlers = handler_groups[self.get("handler_group", "default")] validators = [] c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors, scratch_dir=scratch_dir, gzipped_output=gzip_output) c.run()
def do_run(args): handlers = [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler() ] c = Custodian(handlers, get_runs(args), max_errors=10, gzipped_output=args.gzip) c.run() logging.info("Geometry optimization complete")
def run_task(self, fw_spec): dec = MontyDecoder() jobs = dec.process_decoded(self["jobs"]) fw_env = fw_spec.get("_fw_env", {}) #Override VASP and gamma VASP commands using fw_env if fw_env.get("vasp_cmd"): for j in jobs: j.vasp_cmd = os.path.expandvars(fw_env["vasp_cmd"]) j.gamma_vasp_cmd = j.gamma_vasp_cmd logging.info("Vasp command is {}".format(j.vasp_cmd)) if fw_env.get("gamma_vasp_cmd"): for j in jobs: j.gamma_vasp_cmd = os.path.expandvars(fw_env["gamma_vasp_cmd"]) logging.info("Vasp gamma command is {}".format( j.gamma_vasp_cmd)) #Override custodian scratch dir. cust_params = self.get("custodian_params", {}) if fw_env.get("scratch_root"): cust_params["scratch_dir"] = os.path.expandvars( fw_env["scratch_root"]) logging.info("Running with custodian params %s" % cust_params) handlers = [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler() ] validators = [VasprunXMLValidator()] c = Custodian(handlers=[h.as_dict() for h in handlers], jobs=jobs, validators=[v.as_dict() for v in validators], **cust_params) output = c.run() return FWAction(stored_data=output)
def structure_to_wf(structure): """ This method starts with a Structure object and creates a Workflow object The workflow has two steps - a structure relaxation and a static run :param structure: :return: """ fws = [] # list of FireWorks to run connections = defaultdict(list) # dependencies between FireWorks # generate VASP input objects for 1st VASP run - this is put in the FW spec mpvis = MPGGAVaspInputSet(user_incar_settings={'NPAR': 2}) incar = mpvis.get_incar(structure) poscar = mpvis.get_poscar(structure) kpoints = mpvis.get_kpoints(structure) potcar = mpvis.get_potcar(structure) # serialize the VASP input objects to the FW spec spec = {} spec['vasp'] = {} spec['vasp']['incar'] = incar.as_dict() spec['vasp']['poscar'] = poscar.as_dict() spec['vasp']['kpoints'] = kpoints.as_dict() spec['vasp']['potcar'] = potcar.as_dict() spec['vaspinputset_name'] = mpvis.__class__.__name__ spec['task_type'] = 'GGA optimize structure (2x) example' # set up the custodian that we want to run jobs = VaspJob.double_relaxation_run('') for j in jobs: # turn off auto npar, it doesn't work for >1 node j.auto_npar = False handlers = [ VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler(), NonConvergingErrorHandler() ] c_params = { 'jobs': [j.as_dict() for j in jobs], 'handlers': [h.as_dict() for h in handlers], 'max_errors': 5 } custodiantask = VaspCustodianTaskEx(c_params) # 1st Firework - run GGA optimize structure # VaspWriterTask - write input files (INCAR, POSCAR, KPOINTS, POSCAR) based on spec # CustodianTaskEx - run VASP within a custodian tasks = [VaspWriterTask(), custodiantask] fws.append( Firework(tasks, spec, name=get_name(structure, spec['task_type']), fw_id=1)) # 2nd Firework - insert previous run into DB spec = {'task_type': 'VASP db insertion example'} fws.append( Firework([VaspToDBTaskEx()], spec, name=get_name(structure, spec['task_type']), fw_id=2)) connections[1] = [2] # 3rd Firework - static run. # VaspCopyTask - copy output from previous run to this directory # SetupStaticRunTask - override old parameters for static run # CustodianTaskEx - run VASP within a custodian spec = {'task_type': 'GGA static example'} copytask = VaspCopyTask({'use_CONTCAR': True, 'skip_CHGCAR': True}) setuptask = SetupStaticRunTask() custodiantask = VaspCustodianTaskEx({ 'jobs': [VaspJob('', auto_npar=False).as_dict()], 'handlers': [h.as_dict() for h in handlers], 'max_errors': 5 }) fws.append( Firework([copytask, setuptask, custodiantask], spec, name=get_name(structure, spec['task_type']), fw_id=3)) connections[2] = [3] # 4th Firework - insert previous run into DB spec = {'task_type': 'VASP db insertion example'} fws.append( Firework([VaspToDBTaskEx()], spec, name=get_name(structure, spec['task_type']), fw_id=4)) connections[3] = [4] return Workflow(fws, connections, name=get_slug(structure.formula))
def run_task(self, fw_spec): handler_groups = { "default": [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(), DriftErrorHandler() ], "strict": [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(), AliasingErrorHandler(), DriftErrorHandler() ], "md": [VaspErrorHandler(), NonConvergingErrorHandler()], "no_handler": [] } vasp_cmd = env_chk(self["vasp_cmd"], fw_spec) if isinstance(vasp_cmd, str): vasp_cmd = os.path.expandvars(vasp_cmd) vasp_cmd = shlex.split(vasp_cmd) # initialize variables job_type = self.get("job_type", "normal") scratch_dir = env_chk(self.get("scratch_dir"), fw_spec) gzip_output = self.get("gzip_output", True) max_errors = self.get("max_errors", CUSTODIAN_MAX_ERRORS) auto_npar = env_chk(self.get("auto_npar"), fw_spec, strict=False, default=False) gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"), fw_spec, strict=False, default=None) if gamma_vasp_cmd: gamma_vasp_cmd = shlex.split(gamma_vasp_cmd) # construct jobs if job_type == "normal": jobs = [ VaspJob(vasp_cmd, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd) ] elif job_type == "double_relaxation_run": jobs = VaspJob.double_relaxation_run( vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"), half_kpts_first_relax=self.get("half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX)) elif job_type == "metagga_opt_run": jobs = VaspJob.metagga_opt_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"), half_kpts_first_relax=self.get( "half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX)) elif job_type == "full_opt_run": jobs = VaspJob.full_opt_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"), max_steps=9, half_kpts_first_relax=self.get( "half_kpts_first_relax", HALF_KPOINTS_FIRST_RELAX)) elif job_type == "neb": # TODO: @shyuep @HanmeiTang This means that NEB can only be run (i) in reservation mode # and (ii) when the queueadapter parameter is overridden and (iii) the queue adapter # has a convention for nnodes (with that name). Can't the number of nodes be made a # parameter that the user sets differently? e.g., fw_spec["neb_nnodes"] must be set # when setting job_type=NEB? Then someone can use this feature in non-reservation # mode and without this complication. -computron nnodes = int(fw_spec["_queueadapter"]["nnodes"]) # TODO: @shyuep @HanmeiTang - I am not sure what the code below is doing. It looks like # it is trying to override the number of processors. But I tried running the code # below after setting "vasp_cmd = 'mpirun -n 16 vasp'" and the code fails. # (i) Is this expecting an array vasp_cmd rather than String? If so, that's opposite to # the rest of this task's convention and documentation # (ii) can we get rid of this hacking in the first place? e.g., allowing the user to # separately set the NEB_VASP_CMD as an env_variable and not rewriting the command # inside this. # -computron # Index the tag "-n" or "-np" index = [i for i, s in enumerate(vasp_cmd) if '-n' in s] ppn = int(vasp_cmd[index[0] + 1]) vasp_cmd[index[0] + 1] = str(nnodes * ppn) # Do the same for gamma_vasp_cmd if gamma_vasp_cmd: index = [i for i, s in enumerate(gamma_vasp_cmd) if '-n' in s] ppn = int(gamma_vasp_cmd[index[0] + 1]) gamma_vasp_cmd[index[0] + 1] = str(nnodes * ppn) jobs = [ VaspNEBJob(vasp_cmd, final=False, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd) ] else: raise ValueError("Unsupported job type: {}".format(job_type)) # construct handlers handler_group = self.get("handler_group", "default") if isinstance(handler_group, str): handlers = handler_groups[handler_group] else: handlers = handler_group if self.get("max_force_threshold"): handlers.append( MaxForceErrorHandler( max_force_threshold=self["max_force_threshold"])) if self.get("wall_time"): handlers.append(WalltimeHandler(wall_time=self["wall_time"])) if job_type == "neb": validators = [ ] # CINEB vasprun.xml sometimes incomplete, file structure different else: validators = [VasprunXMLValidator(), VaspFilesValidator()] c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors, scratch_dir=scratch_dir, gzipped_output=gzip_output) c.run() if os.path.exists(zpath("custodian.json")): stored_custodian_data = { "custodian": loadfn(zpath("custodian.json")) } return FWAction(stored_data=stored_custodian_data)
def run_task(self, fw_spec): handler_groups = { "default": [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler() ], "strict": [ VaspErrorHandler(), MeshSymmetryErrorHandler(), UnconvergedErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), AliasingErrorHandler() ], "md": [VaspErrorHandler(), NonConvergingErrorHandler()], "no_handler": [] } vasp_cmd = env_chk(self["vasp_cmd"], fw_spec) if isinstance(vasp_cmd, six.string_types): vasp_cmd = os.path.expandvars(vasp_cmd) vasp_cmd = shlex.split(vasp_cmd) # initialize variables job_type = self.get("job_type", "normal") scratch_dir = env_chk(self.get("scratch_dir"), fw_spec) gzip_output = self.get("gzip_output", True) max_errors = self.get("max_errors", 5) auto_npar = env_chk(self.get("auto_npar"), fw_spec, strict=False, default=False) gamma_vasp_cmd = env_chk(self.get("gamma_vasp_cmd"), fw_spec, strict=False, default=None) if gamma_vasp_cmd: gamma_vasp_cmd = shlex.split(gamma_vasp_cmd) # construct jobs if job_type == "normal": jobs = [ VaspJob(vasp_cmd, auto_npar=auto_npar, gamma_vasp_cmd=gamma_vasp_cmd) ] elif job_type == "double_relaxation_run": jobs = VaspJob.double_relaxation_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"), half_kpts_first_relax=False) elif job_type == "full_opt_run": jobs = VaspJob.full_opt_run(vasp_cmd, auto_npar=auto_npar, ediffg=self.get("ediffg"), max_steps=5, half_kpts_first_relax=False) else: raise ValueError("Unsupported job type: {}".format(job_type)) # construct handlers handlers = handler_groups[self.get("handler_group", "default")] if self.get("max_force_threshold"): handlers.append( MaxForceErrorHandler( max_force_threshold=self["max_force_threshold"])) if self.get("wall_time"): handlers.append(WalltimeHandler(wall_time=self["wall_time"])) validators = [VasprunXMLValidator()] c = Custodian(handlers, jobs, validators=validators, max_errors=max_errors, scratch_dir=scratch_dir, gzipped_output=gzip_output) c.run()
import sys from custodian.custodian import Custodian from custodian.vasp.handlers import VaspErrorHandler, \ UnconvergedErrorHandler, AliasingErrorHandler, FrozenJobErrorHandler, \ PositiveEnergyErrorHandler, MeshSymmetryErrorHandler from custodian.vasp.jobs import VaspJob handlers = [ VaspErrorHandler(), UnconvergedErrorHandler(), AliasingErrorHandler(), FrozenJobErrorHandler(), PositiveEnergyErrorHandler(), MeshSymmetryErrorHandler() ] jobs = [VaspJob(sys.argv[1:])] c = Custodian(handlers, jobs, max_errors=10) c.run()
from pymatgen.io.vasp.inputs import Incar, Poscar, VaspInput,Potcar, Kpoints import os,shutil from custodian.vasp.jobs import VaspJob from custodian.vasp.handlers import VaspErrorHandler, UnconvergedErrorHandler,MeshSymmetryErrorHandler, NonConvergingErrorHandler, PotimErrorHandler from custodian.vasp.validators import VasprunXMLValidator from custodian.custodian import Custodian inc=Incar.from_file("INCAR") pot=Potcar.from_file("POTCAR") pos=Poscar.from_file("POSCAR") kp=Kpoints.from_file("KPOINTS") shutil.copy2('/users/knc6/bin/vdw_kernel.bindat','./') vinput = VaspInput.from_directory(".") job=VaspJob(['mpirun', '-np', '16', '/users/knc6/VASP/vasp54/src/vasp.5.4.1/bin/vasp_std'], final=False, backup=False) handlers = [VaspErrorHandler(), MeshSymmetryErrorHandler(),UnconvergedErrorHandler(), NonConvergingErrorHandler(),PotimErrorHandler()] validators = [VasprunXMLValidator()] c = Custodian(handlers, [job],max_errors=5,validators=validators) c.run()
def get_aneb_wf( structure, working_ion, insert_coords, insert_coords_combinations, n_images, vasp_input_set=None, override_default_vasp_params=None, handler_group=None, selective_dynamics_scheme="fix_two_atom", launch_mode="all", vasp_cmd=VASP_CMD, db_file=DB_FILE, wall_time=None, additional_fields=None, tags=None, powerup_dicts=None, name="ApproxNEB", ): """ Workflow for running the "ApproxNEB" algorithm to estimate energetic barriers for a working ion in a structure (host) between end point positions specified by insert_coords and insert_coords_combinations. Note this workflow is only intended for the dilute lattice limit (where one working ion is in a large supercell structure of the host and little volume change upon insertion is expected). By default workflow sets appropriate VASP input parameters and Custodian handler groups. This workflow uses an "approx_neb" collection to organize outputs and generate inputs for new VASP calculations for easier data management and analysis. An "approx_neb" additional field is automatically added to all task docs generated to assist record keeping. To make modifications to docs generated by this workflow, use of the additional_fields and tags arguments is recommended to ensure all fireworks, tasks collection docs, and approx_neb collection docs are modified. Args: structure (Structure): structure of empty host working_ion: specie of site to insert in structure (e.g. "Li"). insert_coords (1x3 array or list of 1x3 arrays): fractional coordinates of site(s) to insert in structure (e.g. [[0,0,0], [0,0.25,0], [0.5,0,0]]). insert_coords_combinations (list of strings): list of strings corresponding to the list index of insert_coords to specify which combination of end_points to use for path interpolation. (e.g. ["0+1", "0+2"]) n_images: n_images (int): number of images interpolated between end point structures for each path set by insert_coords_combinations vasp_input_set (VaspInputSet class): can use to define VASP input parameters. See pymatgen.io.vasp.sets module for more information. MPRelaxSet() and override_default_vasp_params are used if vasp_input_set = None. override_default_vasp_params (dict): if provided, vasp_input_set is disregarded and the Vasp Input Set is created by passing override_default_vasp_params to MPRelaxSet(). Allows for easy modification of MPRelaxSet(). For example, to set ISIF=2 in the INCAR use: {"user_incar_settings":{"ISIF":2}} handler_group (str or [ErrorHandler]): group of handlers to use for RunVaspCustodian firetask. See handler_groups dict in the code for the groups and complete list of handlers in each group. Alternatively, you can specify a list of ErrorHandler objects. selective_dynamics_scheme (str): "fix_two_atom" launch_mode (str): "all" or "screening" vasp_cmd (str): the name of the full executable for running VASP. db_file (str): path to file containing the database credentials. wall_time (int): Total walltime in seconds. If this is None and the job is running on a PBS system, the handler will attempt to determine the walltime from the PBS_WALLTIME environment variable. If the wall time cannot be determined or is not set, this handler will have no effect. additional_fields (dict): specifies more information to be stored in the approx_neb collection to assist user record keeping. tags (list): list of strings to be stored in the approx_neb collection under the "tags" field to assist user record keeping. powerup_dicts (list): additional powerups given to all the dynamically created image fireworks name (str): name for the workflow returned Returns: Workflow """ approx_neb_params = override_default_vasp_params or { "user_incar_settings": { "EDIFF": 0.0005, "EDIFFG": -0.05, "IBRION": 1, "ISIF": 3, "ISMEAR": 0, "LDAU": False, "NSW": 400, "ADDGRID": True, "ISYM": 1, "NELMIN": 4, } } handler_group = handler_group or [ VaspErrorHandler(), MeshSymmetryErrorHandler(), NonConvergingErrorHandler(), PotimErrorHandler(), PositiveEnergyErrorHandler(), FrozenJobErrorHandler(), StdErrHandler(), WalltimeHandler(wall_time=wall_time), ] wf_uuid = str(uuid4()) additional_fields = deepcopy(additional_fields) host_fw = HostFW( structure=structure, approx_neb_wf_uuid=wf_uuid, db_file=db_file, vasp_input_set=vasp_input_set, vasp_cmd=vasp_cmd, override_default_vasp_params=deepcopy(approx_neb_params), additional_fields=additional_fields, tags=tags, ) # modifies incar settings needed for end point and image structure relaxations if "user_incar_settings" not in approx_neb_params.keys(): approx_neb_params = {"user_incar_settings": {}} approx_neb_params["user_incar_settings"]["ISIF"] = 2 approx_neb_params["user_incar_settings"]["ISYM"] = 0 approx_neb_params["user_incar_settings"]["LDAU"] = False end_point_fws = [] for n, coord in enumerate(insert_coords): end_point_fws.append( EndPointFW( approx_neb_wf_uuid=wf_uuid, insert_specie=working_ion, insert_coords=coord, end_points_index=n, db_file=db_file, override_default_vasp_params=approx_neb_params, parents=host_fw, )) evaluate_path_fws = [] for end_points_combo in insert_coords_combinations: if isinstance(end_points_combo, (str)): combo = end_points_combo.split("+") if len(combo) == 2: c = [int(combo[0]), int(combo[-1])] else: raise ValueError( "string format in insert_coords_combinations is incorrect") evaluate_path_fws.append( EvaluatePathFW( approx_neb_wf_uuid=wf_uuid, end_points_combo=end_points_combo, mobile_specie=working_ion, n_images=n_images, selective_dynamics_scheme=selective_dynamics_scheme, launch_mode=launch_mode, vasp_cmd=vasp_cmd, db_file=db_file, override_default_vasp_params=approx_neb_params, handler_group=handler_group, parents=[end_point_fws[c[0]], end_point_fws[c[1]]], add_additional_fields=additional_fields, add_tags=tags, )) wf = Workflow([host_fw] + end_point_fws + evaluate_path_fws) wf = use_custodian(wf, custodian_params={"handler_group": handler_group}) if isinstance(tags, (list)): wf = add_tags(wf, tags) if isinstance(additional_fields, (dict)): wf = add_additional_fields_to_taskdocs(wf, update_dict=additional_fields) if powerup_dicts is not None: wf = powerup_by_kwargs(wf, powerup_dicts) for fw in wf.fws: fw.spec["vasp_powerups"] = powerup_dicts wf.metadata.update({"approx_neb_wf_uuid": wf_uuid}) wf.name = name return wf