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 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):
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()
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 _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)
else:
jobs = [VaspJob(v_exe)]
handlers = [VaspErrorHandler(), FrozenJobErrorHandler(), MeshSymmetryErrorHandler()]
params = {'jobs': [j.to_dict for j in jobs],
'handlers': [h.to_dict for h in handlers], 'max_errors': 10, 'auto_npar': False, 'auto_gamma': False}
return VaspCustodianTask(params)
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, gzipped=False)
elif 'static' 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 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('', gzipped=False)
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 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 launch_workflow(
self,
launchpad_dir="",
k_product=50,
job=None,
user_incar_settings=None,
potcar_functional="PBE",
additional_handlers=[],
):
"""
Creates a list of Fireworks. Each Firework represents calculations
that will be done on a slab system of a compound in a specific
orientation. Each Firework contains a oriented unit cell relaxation job
and a WriteSlabVaspInputs which creates os. Firework(s) depending
on whether or not Termination=True. Vasp outputs from all slab and
oriented unit cell calculations will then be inserted into a database.
Args:
launchpad_dir (str path): The path to my_launchpad.yaml. Defaults to
the current working directory containing your runs
k_product: kpts[0][0]*a. Decide k density without
kpoint0, default to 50
cwd: (str path): The curent working directory. Location of where you
want your vasp outputs to be.
job (VaspJob): The command (cmd) entered into VaspJob object. Default
is specifically set for running vasp jobs on Carver at NERSC
(use aprun for Hopper or Edison).
user_incar_settings(dict): A dict specifying additional incar
settings, default to None (ediff_per_atom=False)
potcar_functional (str): default to PBE
"""
launchpad = LaunchPad.from_file(os.path.join(os.environ["HOME"], launchpad_dir, "my_launchpad.yaml"))
if self.reset:
launchpad.reset("", require_password=False)
# Scratch directory reffered to by custodian.
# May be different on non-Nersc systems.
if not job:
job = VaspJob(["mpirun", "-n", "64", "vasp"], auto_npar=False, copy_magmom=True)
handlers = [
VaspErrorHandler(),
NonConvergingErrorHandler(),
UnconvergedErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(timeout=3600),
]
if additional_handlers:
handlers.extend(additional_handlers)
cust_params = {
"custodian_params": {"scratch_dir": os.path.join("/global/scratch2/sd/", os.environ["USER"])},
"jobs": job.double_relaxation_run(job.vasp_cmd, auto_npar=False),
"handlers": handlers,
"max_errors": 100,
} # will return a list of jobs
# instead of just being one job
fws = []
for key in self.miller_dict.keys():
# Enumerate through all compounds in the dictionary,
# the key is the compositional formula of the compound
print key
for miller_index in self.miller_dict[key]:
# Enumerates through all miller indices we
# want to create slabs of that compound from
print str(miller_index)
max_norm = max(miller_index) if self.max_normal_search else None
# Whether or not we want to use the
# max_normal_search algorithm from surface.py
print "true or false max norm is ", max_norm, self.max_normal_search
slab = SlabGenerator(
self.unit_cells_dict[key][0], miller_index, self.ssize, self.vsize, max_normal_search=max_norm
)
oriented_uc = slab.oriented_unit_cell
if self.fail_safe and len(oriented_uc) > 199:
break
# This method only creates the oriented unit cell, the
# slabs are created in the WriteSlabVaspInputs task.
# WriteSlabVaspInputs will create the slabs from
# the contcar of the oriented unit cell calculation
handler = []
tasks = []
folderbulk = "/%s_%s_k%s_s%sv%s_%s%s%s" % (
oriented_uc.composition.reduced_formula,
"bulk",
k_product,
self.ssize,
self.vsize,
str(miller_index[0]),
str(miller_index[1]),
str(miller_index[2]),
)
cwd = os.getcwd()
if self.get_bulk_e:
tasks.extend(
[
WriteUCVaspInputs(
oriented_ucell=oriented_uc,
folder=folderbulk,
cwd=cwd,
user_incar_settings=user_incar_settings,
potcar_functional=potcar_functional,
k_product=k_product,
),
RunCustodianTask(dir=folderbulk, cwd=cwd, **cust_params),
VaspSlabDBInsertTask(
struct_type="oriented_unit_cell",
loc=folderbulk,
cwd=cwd,
miller_index=miller_index,
**self.vaspdbinsert_params
),
]
)
# Slab will inherit average final magnetic moment
# of the bulk from outcar, will have to generalize
# this for systems with different elements later
# element = oriented_uc.species[0]
# out = Outcar(cwd+folderbulk)
# out_mag = out.magnetization
# tot_mag = [mag['tot'] for mag in out_mag]
# magmom = np.mean(tot_mag)
# user_incar_settings['MAGMOM'] = {element: magmom}
tasks.append(
WriteSlabVaspInputs(
folder=folderbulk,
cwd=cwd,
user_incar_settings=user_incar_settings,
terminations=self.terminations,
custodian_params=cust_params,
vaspdbinsert_parameters=self.vaspdbinsert_params,
potcar_functional=potcar_functional,
k_product=k_product,
miller_index=miller_index,
min_slab_size=self.ssize,
min_vacuum_size=self.vsize,
ucell=self.unit_cells_dict[key][0],
)
)
fw = Firework(tasks, name=folderbulk)
fws.append(fw)
wf = Workflow(fws, name="Surface Calculations")
launchpad.add_wf(wf)
def test_static(self):
# Just a basic test of init.
VaspJob.double_relaxation_run(["vasp"])
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()
def test_static(self):
# Just a basic test of init.
VaspJob.double_relaxation_run(["vasp"])
def launch_workflow(self,
launchpad_dir="",
k_product=50,
job=None,
user_incar_settings=None,
potcar_functional='PBE',
additional_handlers=[]):
"""
Creates a list of Fireworks. Each Firework represents calculations
that will be done on a slab system of a compound in a specific
orientation. Each Firework contains a oriented unit cell relaxation job
and a WriteSlabVaspInputs which creates os. Firework(s) depending
on whether or not Termination=True. Vasp outputs from all slab and
oriented unit cell calculations will then be inserted into a database.
Args:
launchpad_dir (str path): The path to my_launchpad.yaml. Defaults to
the current working directory containing your runs
k_product: kpts[0][0]*a. Decide k density without
kpoint0, default to 50
cwd: (str path): The curent working directory. Location of where you
want your vasp outputs to be.
job (VaspJob): The command (cmd) entered into VaspJob object. Default
is specifically set for running vasp jobs on Carver at NERSC
(use aprun for Hopper or Edison).
user_incar_settings(dict): A dict specifying additional incar
settings, default to None (ediff_per_atom=False)
potcar_functional (str): default to PBE
"""
launchpad = LaunchPad.from_file(
os.path.join(os.environ["HOME"], launchpad_dir,
"my_launchpad.yaml"))
if self.reset:
launchpad.reset('', require_password=False)
# Scratch directory reffered to by custodian.
# May be different on non-Nersc systems.
if not job:
job = VaspJob(["mpirun", "-n", "64", "vasp"],
auto_npar=False,
copy_magmom=True)
handlers = [
VaspErrorHandler(),
NonConvergingErrorHandler(),
UnconvergedErrorHandler(),
PotimErrorHandler(),
PositiveEnergyErrorHandler(),
FrozenJobErrorHandler(timeout=3600)
]
if additional_handlers:
handlers.extend(additional_handlers)
cust_params = {
"custodian_params": {
"scratch_dir":
os.path.join("/global/scratch2/sd/", os.environ["USER"])
},
"jobs": job.double_relaxation_run(job.vasp_cmd, auto_npar=False),
"handlers": handlers,
"max_errors": 100
} # will return a list of jobs
# instead of just being one job
fws = []
for key in self.miller_dict.keys():
# Enumerate through all compounds in the dictionary,
# the key is the compositional formula of the compound
print key
for miller_index in self.miller_dict[key]:
# Enumerates through all miller indices we
# want to create slabs of that compound from
print str(miller_index)
max_norm = max(
miller_index) if self.max_normal_search else None
# Whether or not we want to use the
# max_normal_search algorithm from surface.py
print 'true or false max norm is ', max_norm, self.max_normal_search
slab = SlabGenerator(self.unit_cells_dict[key][0],
miller_index,
self.ssize,
self.vsize,
max_normal_search=max_norm)
oriented_uc = slab.oriented_unit_cell
if self.fail_safe and len(oriented_uc) > 199:
break
# This method only creates the oriented unit cell, the
# slabs are created in the WriteSlabVaspInputs task.
# WriteSlabVaspInputs will create the slabs from
# the contcar of the oriented unit cell calculation
handler = []
tasks = []
folderbulk = '/%s_%s_k%s_s%sv%s_%s%s%s' % (
oriented_uc.composition.reduced_formula, 'bulk', k_product,
self.ssize, self.vsize, str(miller_index[0]),
str(miller_index[1]), str(miller_index[2]))
cwd = os.getcwd()
if self.get_bulk_e:
tasks.extend([
WriteUCVaspInputs(
oriented_ucell=oriented_uc,
folder=folderbulk,
cwd=cwd,
user_incar_settings=user_incar_settings,
potcar_functional=potcar_functional,
k_product=k_product),
RunCustodianTask(dir=folderbulk,
cwd=cwd,
**cust_params),
VaspSlabDBInsertTask(struct_type="oriented_unit_cell",
loc=folderbulk,
cwd=cwd,
miller_index=miller_index,
**self.vaspdbinsert_params)
])
# Slab will inherit average final magnetic moment
# of the bulk from outcar, will have to generalize
# this for systems with different elements later
# element = oriented_uc.species[0]
# out = Outcar(cwd+folderbulk)
# out_mag = out.magnetization
# tot_mag = [mag['tot'] for mag in out_mag]
# magmom = np.mean(tot_mag)
# user_incar_settings['MAGMOM'] = {element: magmom}
tasks.append(
WriteSlabVaspInputs(
folder=folderbulk,
cwd=cwd,
user_incar_settings=user_incar_settings,
terminations=self.terminations,
custodian_params=cust_params,
vaspdbinsert_parameters=self.vaspdbinsert_params,
potcar_functional=potcar_functional,
k_product=k_product,
miller_index=miller_index,
min_slab_size=self.ssize,
min_vacuum_size=self.vsize,
ucell=self.unit_cells_dict[key][0]))
fw = Firework(tasks, name=folderbulk)
fws.append(fw)
wf = Workflow(fws, name='Surface Calculations')
launchpad.add_wf(wf)
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
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=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, six.string_types):
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")):
return FWAction(stored_data=loadfn(zpath("custodian.json")))
__date__ = "3/20/17"
__email__ = "z9wang at ucsd.edu"
MODULE_DIR = os.path.dirname(os.path.abspath(__file__))
launchpad = LaunchPad.from_file(
os.path.join(os.environ["HOME"], ".fireworks", "my_launchpad.yaml"))
struct_file = os.path.join(MODULE_DIR, "test_files/ICSD_182730_Si.cif")
material_id = "material_id"
s = Structure.from_file(struct_file)
vasp_jobs = VaspJob(
["srun", "-n", "32", "-c", "16", "--cpu_bind=cores", "vasp_std"],
auto_npar=False)
double_relaxations = VaspJob.double_relaxation_run(vasp_cmd=vasp_jobs,
auto_npar=False)
scratch_dir = "/global/cscratch1/sd/{}/temp_project".format(os.environ["USER"])
fw1 = Firework([
MPRelaxationVASPInputTask(structure=s.as_dict()),
RunCustodianTask(jobs=[j.as_dict() for j in double_relaxations],
custodian_params={"scratch_dir": scratch_dir}),
TransferResultsTask(material_id=material_id, job_type="relax")
],
name="{} MP Relax".format(material_id))
fw2 = Firework([
MPStaticVASPInputTask(material_id=material_id),
RunCustodianTask(jobs=[vasp_jobs.as_dict()],
custodian_params={"scratch_dir": scratch_dir}),
