Source code for custodian.vasp.jobs

# coding: utf-8

"""
This module implements basic kinds of jobs for VASP runs.
"""

import logging
import math
import os
import shutil
import subprocess

import numpy as np
from monty.os.path import which
from monty.serialization import dumpfn, loadfn
from monty.shutil import decompress_dir
from pymatgen.core.structure import Structure
from pymatgen.io.vasp.inputs import VaspInput, Incar, Poscar, Kpoints
from pymatgen.io.vasp.outputs import Vasprun, Outcar

from custodian.custodian import Job, SENTRY_DSN
from custodian.utils import backup
from custodian.vasp.handlers import VASP_BACKUP_FILES
from custodian.vasp.interpreter import VaspModder

logger = logging.getLogger(__name__)


VASP_INPUT_FILES = {"INCAR", "POSCAR", "POTCAR", "KPOINTS"}

VASP_OUTPUT_FILES = [
    "DOSCAR",
    "INCAR",
    "KPOINTS",
    "POSCAR",
    "PROCAR",
    "vasprun.xml",
    "CHGCAR",
    "CHG",
    "EIGENVAL",
    "OSZICAR",
    "WAVECAR",
    "CONTCAR",
    "IBZKPT",
    "OUTCAR",
]

VASP_NEB_INPUT_FILES = {"INCAR", "POTCAR", "KPOINTS"}

VASP_NEB_OUTPUT_FILES = ["INCAR", "KPOINTS", "POTCAR", "vasprun.xml"]

VASP_NEB_OUTPUT_SUB_FILES = [
    "CHG",
    "CHGCAR",
    "CONTCAR",
    "DOSCAR",
    "EIGENVAL",
    "IBZKPT",
    "PCDAT",
    "POSCAR",
    "REPORT",
    "PROCAR",
    "OSZICAR",
    "OUTCAR",
    "WAVECAR",
    "XDATCAR",
]


[docs]class VaspJob(Job): """ A basic vasp job. Just runs whatever is in the directory. But conceivably can be a complex processing of inputs etc. with initialization. """ def __init__( self, vasp_cmd, output_file="vasp.out", stderr_file="std_err.txt", suffix="", final=True, backup=True, auto_npar=False, auto_gamma=True, settings_override=None, gamma_vasp_cmd=None, copy_magmom=False, auto_continue=False, ): """ This constructor is necessarily complex due to the need for flexibility. For standard kinds of runs, it's often better to use one of the static constructors. The defaults are usually fine too. Args: vasp_cmd (str): Command to run vasp as a list of args. For example, if you are using mpirun, it can be something like ["mpirun", "pvasp.5.2.11"] output_file (str): Name of file to direct standard out to. Defaults to "vasp.out". stderr_file (str): Name of file to direct standard error to. Defaults to "std_err.txt". suffix (str): A suffix to be appended to the final output. E.g., to rename all VASP output from say vasp.out to vasp.out.relax1, provide ".relax1" as the suffix. final (bool): Indicating whether this is the final vasp job in a series. Defaults to True. backup (bool): Whether to backup the initial input files. If True, the INCAR, KPOINTS, POSCAR and POTCAR will be copied with a ".orig" appended. Defaults to True. auto_npar (bool): Whether to automatically tune NPAR to be sqrt( number of cores) as recommended by VASP for DFT calculations. Generally, this results in significant speedups. Defaults to True. Set to False for HF, GW and RPA calculations. auto_gamma (bool): Whether to automatically check if run is a Gamma 1x1x1 run, and whether a Gamma optimized version of VASP exists with ".gamma" appended to the name of the VASP executable (typical setup in many systems). If so, run the gamma optimized version of VASP instead of regular VASP. You can also specify the gamma vasp command using the gamma_vasp_cmd argument if the command is named differently. settings_override ([dict]): An ansible style list of dict to override changes. For example, to set ISTART=1 for subsequent runs and to copy the CONTCAR to the POSCAR, you will provide:: [{"dict": "INCAR", "action": {"_set": {"ISTART": 1}}}, {"file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}}] gamma_vasp_cmd (str): Command for gamma vasp version when auto_gamma is True. Should follow the list style of subprocess. Defaults to None, which means ".gamma" is added to the last argument of the standard vasp_cmd. copy_magmom (bool): Whether to copy the final magmom from the OUTCAR to the next INCAR. Useful for multi-relaxation runs where the CHGCAR and WAVECAR are sometimes deleted (due to changes in fft grid, etc.). Only applies to non-final runs. auto_continue (bool): Whether to automatically continue a run if a STOPCAR is present. This is very useful if using the wall-time handler which will write a read-only STOPCAR to prevent VASP from deleting it once it finishes """ self.vasp_cmd = vasp_cmd self.output_file = output_file self.stderr_file = stderr_file self.final = final self.backup = backup self.suffix = suffix self.settings_override = settings_override self.auto_npar = auto_npar self.auto_gamma = auto_gamma self.gamma_vasp_cmd = gamma_vasp_cmd self.copy_magmom = copy_magmom self.auto_continue = auto_continue if SENTRY_DSN: # if using Sentry logging, add specific VASP executable to scope from sentry_sdk import configure_scope with configure_scope() as scope: try: if isinstance(vasp_cmd, str): vasp_path = which(vasp_cmd.split(" ")[-1]) elif isinstance(vasp_cmd, list): vasp_path = which(vasp_cmd[-1]) scope.set_tag("vasp_path", vasp_path) scope.set_tag("vasp_cmd", vasp_cmd) except Exception: logger.error( "Failed to detect VASP path: {}".format(vasp_cmd), exc_info=True ) scope.set_tag("vasp_cmd", vasp_cmd)
[docs] def setup(self): """ Performs initial setup for VaspJob, including overriding any settings and backing up. """ decompress_dir(".") if self.backup: for f in VASP_INPUT_FILES: try: shutil.copy(f, "{}.orig".format(f)) except FileNotFoundError: # handle the situation when there is no KPOINTS file if f == "KPOINTS": pass if self.auto_npar: try: incar = Incar.from_file("INCAR") # Only optimized NPAR for non-HF and non-RPA calculations. if not ( incar.get("LHFCALC") or incar.get("LRPA") or incar.get("LEPSILON") ): if incar.get("IBRION") in [5, 6, 7, 8]: # NPAR should not be set for Hessian matrix # calculations, whether in DFPT or otherwise. del incar["NPAR"] else: import multiprocessing # try sge environment variable first # (since multiprocessing counts cores on the current # machine only) ncores = os.environ.get("NSLOTS") or multiprocessing.cpu_count() ncores = int(ncores) for npar in range(int(math.sqrt(ncores)), ncores): if ncores % npar == 0: incar["NPAR"] = npar break incar.write_file("INCAR") except Exception: pass if self.auto_continue: if os.path.exists("continue.json"): actions = loadfn("continue.json").get("actions") logger.info("Continuing previous VaspJob. Actions: {}".format(actions)) backup(VASP_BACKUP_FILES, prefix="prev_run") VaspModder().apply_actions(actions) else: # Default functionality is to copy CONTCAR to POSCAR and set # ISTART to 1 in the INCAR, but other actions can be specified if self.auto_continue is True: actions = [ { "file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}, }, {"dict": "INCAR", "action": {"_set": {"ISTART": 1}}}, ] else: actions = self.auto_continue dumpfn({"actions": actions}, "continue.json") if self.settings_override is not None: VaspModder().apply_actions(self.settings_override)
[docs] def run(self): """ Perform the actual VASP run. Returns: (subprocess.Popen) Used for monitoring. """ cmd = list(self.vasp_cmd) if self.auto_gamma: vi = VaspInput.from_directory(".") kpts = vi["KPOINTS"] if kpts is not None: if kpts.style == Kpoints.supported_modes.Gamma and tuple( kpts.kpts[0] ) == (1, 1, 1): if self.gamma_vasp_cmd is not None and which( self.gamma_vasp_cmd[-1] ): cmd = self.gamma_vasp_cmd elif which(cmd[-1] + ".gamma"): cmd[-1] += ".gamma" logger.info("Running {}".format(" ".join(cmd))) with open(self.output_file, "w") as f_std, open( self.stderr_file, "w", buffering=1 ) as f_err: # use line buffering for stderr p = subprocess.Popen(cmd, stdout=f_std, stderr=f_err) return p
[docs] def postprocess(self): """ Postprocessing includes renaming and gzipping where necessary. Also copies the magmom to the incar if necessary """ for f in VASP_OUTPUT_FILES + [self.output_file]: if os.path.exists(f): if self.final and self.suffix != "": shutil.move(f, "{}{}".format(f, self.suffix)) elif self.suffix != "": shutil.copy(f, "{}{}".format(f, self.suffix)) if self.copy_magmom and not self.final: try: outcar = Outcar("OUTCAR") magmom = [m["tot"] for m in outcar.magnetization] incar = Incar.from_file("INCAR") incar["MAGMOM"] = magmom incar.write_file("INCAR") except Exception: logger.error("MAGMOM copy from OUTCAR to INCAR failed") # Remove continuation so if a subsequent job is run in # the same directory, will not restart this job. if os.path.exists("continue.json"): os.remove("continue.json")
[docs] @classmethod def double_relaxation_run( cls, vasp_cmd, auto_npar=True, ediffg=-0.05, half_kpts_first_relax=False, auto_continue=False, ): """ Returns a list of two jobs corresponding to an AFLOW style double relaxation run. Args: vasp_cmd (str): Command to run vasp as a list of args. For example, if you are using mpirun, it can be something like ["mpirun", "pvasp.5.2.11"] auto_npar (bool): Whether to automatically tune NPAR to be sqrt( number of cores) as recommended by VASP for DFT calculations. Generally, this results in significant speedups. Defaults to True. Set to False for HF, GW and RPA calculations. ediffg (float): Force convergence criteria for subsequent runs ( ignored for the initial run.) half_kpts_first_relax (bool): Whether to halve the kpoint grid for the first relaxation. Speeds up difficult convergence considerably. Defaults to False. Returns: List of two jobs corresponding to an AFLOW style run. """ incar_update = {"ISTART": 1} if ediffg: incar_update["EDIFFG"] = ediffg settings_overide_1 = None settings_overide_2 = [ {"dict": "INCAR", "action": {"_set": incar_update}}, {"file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}}, ] if ( half_kpts_first_relax and os.path.exists("KPOINTS") and os.path.exists("POSCAR") ): kpts = Kpoints.from_file("KPOINTS") orig_kpts_dict = kpts.as_dict() # lattice vectors with length < 8 will get >1 KPOINT kpts.kpts = ( np.round(np.maximum(np.array(kpts.kpts) / 2, 1)).astype(int).tolist() ) low_kpts_dict = kpts.as_dict() settings_overide_1 = [ {"dict": "KPOINTS", "action": {"_set": low_kpts_dict}} ] settings_overide_2.append( {"dict": "KPOINTS", "action": {"_set": orig_kpts_dict}} ) return [ VaspJob( vasp_cmd, final=False, suffix=".relax1", auto_npar=auto_npar, auto_continue=auto_continue, settings_override=settings_overide_1, ), VaspJob( vasp_cmd, final=True, backup=False, suffix=".relax2", auto_npar=auto_npar, auto_continue=auto_continue, settings_override=settings_overide_2, ), ]
[docs] @classmethod def metagga_opt_run( cls, vasp_cmd, auto_npar=True, ediffg=-0.05, half_kpts_first_relax=False, auto_continue=False, ): """ Returns a list of thres jobs to perform an optimization for any metaGGA functional. There is an initial calculation of the GGA wavefunction which is fed into the initial metaGGA optimization to precondition the electronic structure optimizer. The metaGGA optimization is performed using the double relaxation scheme """ incar = Incar.from_file("INCAR") # Defaults to using the SCAN metaGGA metaGGA = incar.get("METAGGA", "SCAN") # Pre optimze WAVECAR and structure using regular GGA pre_opt_setings = [ { "dict": "INCAR", "action": {"_set": {"METAGGA": None, "LWAVE": True, "NSW": 0}}, } ] jobs = [ VaspJob( vasp_cmd, auto_npar=auto_npar, final=False, suffix=".precondition", settings_override=pre_opt_setings, ) ] # Finish with regular double relaxation style run using SCAN jobs.extend( VaspJob.double_relaxation_run( vasp_cmd, auto_npar=auto_npar, ediffg=ediffg, half_kpts_first_relax=half_kpts_first_relax, ) ) # Ensure the first relaxation doesn't overwrite the original inputs jobs[1].backup = False # Update double_relaxation job to start from pre-optimized run post_opt_settings = [ { "dict": "INCAR", "action": { "_set": { "METAGGA": metaGGA, "ISTART": 1, "NSW": incar.get("NSW", 99), "LWAVE": incar.get("LWAVE", False), } }, }, {"file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}}, ] if jobs[1].settings_override: post_opt_settings = jobs[1].settings_override + post_opt_settings jobs[1].settings_override = post_opt_settings return jobs
[docs] @classmethod def full_opt_run( cls, vasp_cmd, vol_change_tol=0.02, max_steps=10, ediffg=-0.05, half_kpts_first_relax=False, **vasp_job_kwargs ): r""" Returns a generator of jobs for a full optimization run. Basically, this runs an infinite series of geometry optimization jobs until the % vol change in a particular optimization is less than vol_change_tol. Args: vasp_cmd (str): Command to run vasp as a list of args. For example, if you are using mpirun, it can be something like ["mpirun", "pvasp.5.2.11"] vol_change_tol (float): The tolerance at which to stop a run. Defaults to 0.05, i.e., 5%. max_steps (int): The maximum number of runs. Defaults to 10 ( highly unlikely that this limit is ever reached). ediffg (float): Force convergence criteria for subsequent runs ( ignored for the initial run.) half_kpts_first_relax (bool): Whether to halve the kpoint grid for the first relaxation. Speeds up difficult convergence considerably. Defaults to False. \*\*vasp_job_kwargs: Passthrough kwargs to VaspJob. See :class:`custodian.vasp.jobs.VaspJob`. Returns: Generator of jobs. """ for i in range(max_steps): if i == 0: settings = None backup = True if ( half_kpts_first_relax and os.path.exists("KPOINTS") and os.path.exists("POSCAR") ): kpts = Kpoints.from_file("KPOINTS") orig_kpts_dict = kpts.as_dict() kpts.kpts = np.maximum(np.array(kpts.kpts) / 2, 1).tolist() low_kpts_dict = kpts.as_dict() settings = [{"dict": "KPOINTS", "action": {"_set": low_kpts_dict}}] else: backup = False initial = Poscar.from_file("POSCAR").structure final = Poscar.from_file("CONTCAR").structure vol_change = (final.volume - initial.volume) / initial.volume logger.info("Vol change = %.1f %%!" % (vol_change * 100)) if abs(vol_change) < vol_change_tol: logger.info("Stopping optimization!") break incar_update = {"ISTART": 1} if ediffg: incar_update["EDIFFG"] = ediffg settings = [ {"dict": "INCAR", "action": {"_set": incar_update}}, { "file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}, }, ] if i == 1 and half_kpts_first_relax: settings.append( {"dict": "KPOINTS", "action": {"_set": orig_kpts_dict}} ) logger.info("Generating job = %d!" % (i + 1)) yield VaspJob( vasp_cmd, final=False, backup=backup, suffix=".relax%d" % (i + 1), settings_override=settings, **vasp_job_kwargs )
[docs] @classmethod def constrained_opt_run( cls, vasp_cmd, lattice_direction, initial_strain, atom_relax=True, max_steps=20, algo="bfgs", **vasp_job_kwargs ): r""" Returns a generator of jobs for a constrained optimization run. Typical use case is when you want to approximate a biaxial strain situation, e.g., you apply a defined strain to a and b directions of the lattice, but allows the c-direction to relax. Some guidelines on the use of this method: i. It is recommended you do not use the Auto kpoint generation. The grid generated via Auto may fluctuate with changes in lattice param, resulting in numerical noise. ii. Make sure your EDIFF/EDIFFG is properly set in your INCAR. The optimization relies on these values to determine convergence. Args: vasp_cmd (str): Command to run vasp as a list of args. For example, if you are using mpirun, it can be something like ["mpirun", "pvasp.5.2.11"] lattice_direction (str): Which direction to relax. Valid values are "a", "b" or "c". initial_strain (float): An initial strain to be applied to the lattice_direction. This can usually be estimated as the negative of the strain applied in the other two directions. E.g., if you apply a tensile strain of 0.05 to the a and b directions, you can use -0.05 as a reasonable first guess for initial strain. atom_relax (bool): Whether to relax atomic positions. max_steps (int): The maximum number of runs. Defaults to 20 ( highly unlikely that this limit is ever reached). algo (str): Algorithm to use to find minimum. Default is "bfgs", which is fast, but can be sensitive to numerical noise in energy calculations. The alternative is "bisection", which is more robust but can be a bit slow. The code does fall back on the bisection when bfgs gives a non-sensical result, e.g., negative lattice params. \*\*vasp_job_kwargs: Passthrough kwargs to VaspJob. See :class:`custodian.vasp.jobs.VaspJob`. Returns: Generator of jobs. At the end of the run, an "EOS.txt" is written which provides a quick look at the E vs lattice parameter. """ nsw = 99 if atom_relax else 0 incar = Incar.from_file("INCAR") # Set the energy convergence criteria as the EDIFFG (if present) or # 10 x EDIFF (which itself defaults to 1e-4 if not present). if incar.get("EDIFFG") and incar.get("EDIFFG") > 0: etol = incar["EDIFFG"] else: etol = incar.get("EDIFF", 1e-4) * 10 if lattice_direction == "a": lattice_index = 0 elif lattice_direction == "b": lattice_index = 1 else: lattice_index = 2 energies = {} for i in range(max_steps): if i == 0: settings = [ {"dict": "INCAR", "action": {"_set": {"ISIF": 2, "NSW": nsw}}} ] structure = Poscar.from_file("POSCAR").structure x = structure.lattice.abc[lattice_index] backup = True else: backup = False v = Vasprun("vasprun.xml") structure = v.final_structure energy = v.final_energy lattice = structure.lattice x = lattice.abc[lattice_index] energies[x] = energy if i == 1: x *= 1 + initial_strain else: # Sort the lattice parameter by energies. min_x = min(energies.keys(), key=lambda e: energies[e]) sorted_x = sorted(energies.keys()) ind = sorted_x.index(min_x) if ind == 0: other = ind + 1 elif ind == len(sorted_x) - 1: other = ind - 1 else: other = ( ind + 1 if energies[sorted_x[ind + 1]] < energies[sorted_x[ind - 1]] else ind - 1 ) if abs(energies[min_x] - energies[sorted_x[other]]) < etol: logger.info( "Stopping optimization! Final %s = %f" % (lattice_direction, min_x) ) break if ind == 0 and len(sorted_x) > 2: # Lowest energy lies outside of range of lowest value. # we decrease the lattice parameter in the next # iteration to find a minimum. This applies only when # there are at least 3 values. x = sorted_x[0] - abs(sorted_x[1] - sorted_x[0]) logger.info( "Lowest energy lies below bounds. " "Setting %s = %f." % (lattice_direction, x) ) elif ind == len(sorted_x) - 1 and len(sorted_x) > 2: # Lowest energy lies outside of range of highest value. # we increase the lattice parameter in the next # iteration to find a minimum. This applies only when # there are at least 3 values. x = sorted_x[-1] + abs(sorted_x[-1] - sorted_x[-2]) logger.info( "Lowest energy lies above bounds. " "Setting %s = %f." % (lattice_direction, x) ) else: if algo.lower() == "bfgs" and len(sorted_x) >= 4: try: # If there are more than 4 data points, we will # do a quadratic fit to accelerate convergence. x1 = list(energies.keys()) y1 = [energies[j] for j in x1] z1 = np.polyfit(x1, y1, 2) pp = np.poly1d(z1) from scipy.optimize import minimize result = minimize( pp, min_x, bounds=[(sorted_x[0], sorted_x[-1])] ) if (not result.success) or result.x[0] < 0: raise ValueError("Negative lattice constant!") x = result.x[0] logger.info( "BFGS minimized %s = %f." % (lattice_direction, x) ) except ValueError as ex: # Fall back on bisection algo if the bfgs fails. logger.info(str(ex)) x = (min_x + sorted_x[other]) / 2 logger.info( "Falling back on bisection %s = %f." % (lattice_direction, x) ) else: x = (min_x + sorted_x[other]) / 2 logger.info("Bisection %s = %f." % (lattice_direction, x)) lattice = lattice.matrix lattice[lattice_index] = ( lattice[lattice_index] / np.linalg.norm(lattice[lattice_index]) * x ) s = Structure(lattice, structure.species, structure.frac_coords) fname = "POSCAR.%f" % x s.to(filename=fname) incar_update = {"ISTART": 1, "NSW": nsw, "ISIF": 2} settings = [ {"dict": "INCAR", "action": {"_set": incar_update}}, {"file": fname, "action": {"_file_copy": {"dest": "POSCAR"}}}, ] logger.info("Generating job = %d with parameter %f!" % (i + 1, x)) yield VaspJob( vasp_cmd, final=False, backup=backup, suffix=".static.%f" % x, settings_override=settings, **vasp_job_kwargs ) with open("EOS.txt", "wt") as f: f.write("# %s energy\n" % lattice_direction) for k in sorted(energies.keys()): f.write("%f %f\n" % (k, energies[k]))
[docs] def terminate(self): """ Ensure all vasp jobs are killed. """ for k in self.vasp_cmd: if "vasp" in k: try: os.system("killall %s" % k) except Exception: pass
[docs]class VaspNEBJob(Job): """ A NEB vasp job, especially for CI-NEB running at PBS clusters. The class is added for the purpose of handling a different folder arrangement in NEB calculation. """ def __init__( self, vasp_cmd, output_file="neb_vasp.out", stderr_file="neb_std_err.txt", suffix="", final=True, backup=True, auto_npar=True, half_kpts=False, auto_gamma=True, auto_continue=False, gamma_vasp_cmd=None, settings_override=None, ): """ This constructor is a simplified version of VaspJob, which satisfies the need for flexibility. For standard kinds of runs, it's often better to use one of the static constructors. The defaults are usually fine too. Args: vasp_cmd (str): Command to run vasp as a list of args. For example, if you are using mpirun, it can be something like ["mpirun", "pvasp.5.2.11"] output_file (str): Name of file to direct standard out to. Defaults to "vasp.out". stderr_file (str): Name of file to direct standard error to. Defaults to "std_err.txt". suffix (str): A suffix to be appended to the final output. E.g., to rename all VASP output from say vasp.out to vasp.out.relax1, provide ".relax1" as the suffix. final (bool): Indicating whether this is the final vasp job in a series. Defaults to True. backup (bool): Whether to backup the initial input files. If True, the INCAR, KPOINTS, POSCAR and POTCAR will be copied with a ".orig" appended. Defaults to True. auto_npar (bool): Whether to automatically tune NPAR to be sqrt( number of cores) as recommended by VASP for DFT calculations. Generally, this results in significant speedups. Defaults to True. Set to False for HF, GW and RPA calculations. half_kpts (bool): Whether to halve the kpoint grid for NEB. Speeds up convergence considerably. Defaults to False. auto_gamma (bool): Whether to automatically check if run is a Gamma 1x1x1 run, and whether a Gamma optimized version of VASP exists with ".gamma" appended to the name of the VASP executable (typical setup in many systems). If so, run the gamma optimized version of VASP instead of regular VASP. You can also specify the gamma vasp command using the gamma_vasp_cmd argument if the command is named differently. auto_continue (bool): Whether to automatically continue a run if a STOPCAR is present. This is very useful if using the wall-time handler which will write a read-only STOPCAR to prevent VASP from deleting it once it finishes. gamma_vasp_cmd (str): Command for gamma vasp version when auto_gamma is True. Should follow the list style of subprocess. Defaults to None, which means ".gamma" is added to the last argument of the standard vasp_cmd. settings_override ([dict]): An ansible style list of dict to override changes. For example, to set ISTART=1 for subsequent runs and to copy the CONTCAR to the POSCAR, you will provide:: [{"dict": "INCAR", "action": {"_set": {"ISTART": 1}}}, {"file": "CONTCAR", "action": {"_file_copy": {"dest": "POSCAR"}}}] """ self.vasp_cmd = vasp_cmd self.output_file = output_file self.stderr_file = stderr_file self.final = final self.backup = backup self.suffix = suffix self.auto_npar = auto_npar self.half_kpts = half_kpts self.auto_gamma = auto_gamma self.gamma_vasp_cmd = gamma_vasp_cmd self.auto_continue = auto_continue self.settings_override = settings_override self.neb_dirs = [] # 00, 01, etc. self.neb_sub = [] # 01, 02, etc. for path in os.listdir("."): if os.path.isdir(path) and path.isdigit(): self.neb_dirs.append(path) self.neb_dirs = sorted(self.neb_dirs) self.neb_sub = self.neb_dirs[1:-1]
[docs] def setup(self): """ Performs initial setup for VaspNEBJob, including overriding any settings and backing up. """ neb_dirs = self.neb_dirs if self.backup: # Back up KPOINTS, INCAR, POTCAR for f in VASP_NEB_INPUT_FILES: shutil.copy(f, "{}.orig".format(f)) # Back up POSCARs for path in neb_dirs: poscar = os.path.join(path, "POSCAR") shutil.copy(poscar, "{}.orig".format(poscar)) if self.half_kpts and os.path.exists("KPOINTS"): kpts = Kpoints.from_file("KPOINTS") kpts.kpts = np.maximum(np.array(kpts.kpts) / 2, 1) kpts.kpts = kpts.kpts.astype(int).tolist() if tuple(kpts.kpts[0]) == (1, 1, 1): kpt_dic = kpts.as_dict() kpt_dic["generation_style"] = "Gamma" kpts = Kpoints.from_dict(kpt_dic) kpts.write_file("KPOINTS") if self.auto_npar: try: incar = Incar.from_file("INCAR") import multiprocessing # Try sge environment variable first # (since multiprocessing counts cores on the current # machine only) ncores = os.environ.get("NSLOTS") or multiprocessing.cpu_count() ncores = int(ncores) for npar in range(int(math.sqrt(ncores)), ncores): if ncores % npar == 0: incar["NPAR"] = npar break incar.write_file("INCAR") except Exception: pass if ( self.auto_continue and os.path.exists("STOPCAR") and not os.access("STOPCAR", os.W_OK) ): # Remove STOPCAR os.chmod("STOPCAR", 0o644) os.remove("STOPCAR") # Copy CONTCAR to POSCAR for path in self.neb_sub: contcar = os.path.join(path, "CONTCAR") poscar = os.path.join(path, "POSCAR") shutil.copy(contcar, poscar) if self.settings_override is not None: VaspModder().apply_actions(self.settings_override)
[docs] def run(self): """ Perform the actual VASP run. Returns: (subprocess.Popen) Used for monitoring. """ cmd = list(self.vasp_cmd) if self.auto_gamma: kpts = Kpoints.from_file("KPOINTS") if kpts.style == Kpoints.supported_modes.Gamma and tuple(kpts.kpts[0]) == ( 1, 1, 1, ): if self.gamma_vasp_cmd is not None and which(self.gamma_vasp_cmd[-1]): cmd = self.gamma_vasp_cmd elif which(cmd[-1] + ".gamma"): cmd[-1] += ".gamma" logger.info("Running {}".format(" ".join(cmd))) with open(self.output_file, "w") as f_std, open( self.stderr_file, "w", buffering=1 ) as f_err: # Use line buffering for stderr p = subprocess.Popen(cmd, stdout=f_std, stderr=f_err) return p
[docs] def postprocess(self): """ Postprocessing includes renaming and gzipping where necessary. """ # Add suffix to all sub_dir/{items} for path in self.neb_dirs: for f in VASP_NEB_OUTPUT_SUB_FILES: f = os.path.join(path, f) if os.path.exists(f): if self.final and self.suffix != "": shutil.move(f, "{}{}".format(f, self.suffix)) elif self.suffix != "": shutil.copy(f, "{}{}".format(f, self.suffix)) # Add suffix to all output files for f in VASP_NEB_OUTPUT_FILES + [self.output_file]: if os.path.exists(f): if self.final and self.suffix != "": shutil.move(f, "{}{}".format(f, self.suffix)) elif self.suffix != "": shutil.copy(f, "{}{}".format(f, self.suffix))
[docs]class GenerateVaspInputJob(Job): """ Generates a VASP input based on an existing directory. This is typically used to modify the VASP input files before the next VaspJob. """ def __init__(self, input_set, contcar_only=True, **kwargs): """ Args: input_set (str): Full path to the input set. E.g., "pymatgen.io.vasp.sets.MPNonSCFSet". contcar_only (bool): If True (default), only CONTCAR structures are used as input to the input set. """ self.input_set = input_set self.contcar_only = contcar_only self.kwargs = kwargs
[docs] def setup(self): """ Dummy setup """ pass
[docs] def run(self): """ Run the calculation. """ if os.path.exists("CONTCAR"): structure = Structure.from_file("CONTCAR") elif (not self.contcar_only) and os.path.exists("POSCAR"): structure = Structure.from_file("POSCAR") else: raise RuntimeError("No CONTCAR/POSCAR detected to generate input!") modname, classname = self.input_set.rsplit(".", 1) mod = __import__(modname, globals(), locals(), [classname], 0) vis = getattr(mod, classname)(structure, **self.kwargs) vis.write_input(".")
[docs] def postprocess(self): """ Dummy postprocess. """ pass