Source code for pymatgen.io.cp2k.inputs

"""
This module defines the building blocks of a CP2K input file. The cp2k input structure is essentially a collection
of "sections" which are similar to dictionary objects that activate modules of the cp2k executable, and then
"keywords" which adjust variables inside of those modules. For example, FORCE_EVAL section will activate CP2K's ability
to calculate forces, and inside FORCE_EVAL, the Keyword "METHOD can be set to "QS" to set the method of force evaluation
to be the quickstep (DFT) module.

A quick overview of the module:

-- Section class defines the basis of Cp2k input and contains methods for manipulating these objects similarly to Dicts.
-- Keyword class defines the keywords used inside of Section objects that changes variables in Cp2k program
-- Cp2kInput class is special instantiation of Section that is used to represent the full cp2k calculation input.
-- The rest of the classes are children of Section intended to make initialization of common sections easier.
"""

import copy
import os
import re
import textwrap
import warnings
from typing import Dict, List, Sequence, Tuple, Union

from monty.io import zopen
from monty.json import MSONable

from pymatgen.core.lattice import Lattice
from pymatgen.core.structure import Molecule, Structure
from pymatgen.io.cp2k.utils import _postprocessor, _preprocessor

__author__ = "Nicholas Winner"
__version__ = "0.3"
__email__ = "nwinner@berkeley.edu"
__date__ = "August 2020"


[docs]class Keyword(MSONable): """ Class representing a keyword argument in CP2K. Within CP2K Secitons, which activate features of the CP2K code, the keywords are arguments that control the functionality of that feature. For example, the section "FORCE_EVAL" activates the evaluation of forces/energies, but within "FORCE_EVAL" the keyword "METHOD" controls whether or not this will be done with, say, "Quickstep" (DFT) or "EIP" (empirical interatomic potential). """ def __init__( self, name: str, *values, description: str = None, units: str = None, verbose: bool = True, repeats: bool = False, ): """ Initializes a keyword. These Keywords and the value passed to them are sometimes as simple as KEYWORD VALUE, but can also be more elaborate such as KEYWORD [UNITS] VALUE1 VALUE2, which is why this class exists: to handle many values and control easy printing to an input file. Args: name (str): The name of this keyword. Must match an acceptable keyword from CP2K args: All non-keyword arguments after 'name' are interpreted as the values to set for this keyword. i.e: KEYWORD ARG1 ARG2 would provide two values to the keyword. description (str): The description for this keyword. This can make readability of input files easier for some. Default=None. units (str): The units for this keyword. If not specified, CP2K default units will be used. Consult manual for default units. Default=None. repeats (bool): Whether or not this keyword may be repeated. Default=False. """ self.name = name self.values = values self.description = description self.repeats = repeats self.units = units self.verbose = verbose def __str__(self): return ( self.name.__str__() + " " + ("[{}] ".format(self.units) if self.units else "") + " ".join(map(str, self.values)) + (" ! " + self.description if (self.description and self.verbose) else "") ) def __eq__(self, other): if self.name.upper() == other.name.upper(): v1 = [_.upper() if isinstance(_, str) else _ for _ in self.values] v2 = [_.upper() if isinstance(_, str) else _ for _ in other.values] if v1 == v2: if self.units == self.units: return True return False def __add__(self, other): return KeywordList(keywords=[self, other]) def __getitem__(self, item): return self.values[item]
[docs] def as_dict(self): """ Get a dictionary representation of the Keyword """ d = {} d["@module"] = self.__class__.__module__ d["@class"] = self.__class__.__name__ d["name"] = self.name d["values"] = self.values d["description"] = self.description d["repeats"] = self.repeats d["units"] = self.units d["verbose"] = self.verbose return d
[docs] def get_string(self): """ String representation of Keyword """ return self.__str__()
[docs] @classmethod def from_dict(cls, d): """ Initialise from dictonary """ return Keyword( d["name"], *d["values"], description=d["description"], repeats=d["repeats"], units=d["units"], verbose=d["verbose"], )
[docs] @staticmethod def from_string(s): """ Initialise from a string """ s = s.strip() if s.__contains__("!") or s.__contains__("#"): s, description = re.split("(?:!|#)", s) description = description.strip() else: description = None units = re.findall(r"\[(.*)\]", s) or [None] s = re.sub(r"\[(.*)\]", "", s) return Keyword(*map(_postprocessor, s.split()), units=units[0], description=description)
[docs] def verbosity(self, v): """ Change the printing of this keyword's description. """ self.verbose = v
[docs]class KeywordList(MSONable): """ Some keywords can be repeated, which makes accessing them via the normal dictionary methods a little unnatural. This class deals with this by defining a collection of same-named keywords that are accessed by one name. """ def __init__(self, keywords: Sequence[Keyword]): """ Initializes a keyword. These Keywords and the value passed to them are sometimes as simple as KEYWORD VALUE, but can also be more elaborate such as KEYWORD [UNITS] VALUE1 VALUE2, which is why this class exists: to handle many values and control easy printing to an input file. Args: keywords: A list of keywords. Must all have the same name (case-insensitive) """ assert all(k.name.upper() == keywords[0].name.upper() for k in keywords) if keywords else True self.name = keywords[0].name if keywords else None self.keywords = keywords def __str__(self): return self.get_string() def __eq__(self, other): return all(k == o for k, o in zip(self.keywords, other.keywords)) def __add__(self, other): return self.extend(other) def __len__(self): return len(self.keywords) def __getitem__(self, item): return self.keywords[item]
[docs] def append(self, item): """ append the keyword list """ self.keywords.append(item)
[docs] def extend(self, l): """ extend the keyword list """ self.keywords.extend(l)
[docs] def get_string(self, indent=0): """ String representation of Keyword """ return " \n".join(["\t" * indent + k.__str__() for k in self.keywords])
[docs] def verbosity(self, verbosity): """ Silence all keywords in keyword list """ for k in self.keywords: k.verbosity(verbosity)
[docs]class Section(MSONable): """ Basic input representation of input to Cp2k. Activates functionality inside of the Cp2k executable. """ required_sections: tuple = () required_keywords: tuple = () def __init__( self, name: str, subsections: dict = None, repeats: bool = False, description: Union[str, None] = None, keywords: Dict = {}, section_parameters: Union[List, Tuple] = [], location: str = None, verbose: bool = True, alias: str = None, **kwargs, ): """ Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K's ability to calculate forces. Sections are described with the following: Args: name (str): The name of the section (must match name in CP2K) subsections (dict): A dictionary of subsections that are nested in this section. Format is {'NAME': Section(*args, **kwargs). The name you chose for 'NAME' to index that subsection does not *have* to be the same as the section's true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later. repeats (bool): Whether or not this section can be repeated. Most sections cannot. Default=False. description (str): Description of this section for easier readability/more descriptiveness keywords (list): the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below). section_parameters (list): the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects. location (str): the path to the section in the form 'SECTION/SUBSECTION1/SUBSECTION3', example for QS module: 'FORCE_EVAL/DFT/QS'. This location is used to automatically determine if a subsection requires a supersection to be activated. verbose (str): Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True. kwargs are interpreted as keyword, value pairs and added to the keywords array as Keyword objects """ self.name = name self.subsections = subsections if subsections is not None else {} self.repeats = repeats self.description = description self.keywords = keywords self.section_parameters = section_parameters self.location = location self.verbose = verbose self.alias = alias self.kwargs = kwargs for k, v in self.kwargs.items(): self.keywords[k] = Keyword(k, v) for k in self.required_sections: if not self.check(k): raise UserWarning("WARNING: REQUIRED SECTION {} HAS NOT BEEN INITIALIZED".format(k)) for k in self.required_keywords: if k not in self.keywords: raise UserWarning("WARNING: REQUIRED KEYWORD {} HAS NOT BEEN PROVIDED".format(k)) def __str__(self): return self.get_string() def __eq__(self, d): d2 = copy.deepcopy(d) s2 = copy.deepcopy(self) d2.silence() s2.silence() return d2.as_dict() == s2.as_dict() def __deepcopy__(self, memodict={}): c = copy.deepcopy(self.as_dict()) return getattr(__import__(c["@module"], globals(), locals(), c["@class"], 0), c["@class"]).from_dict( copy.deepcopy(self.as_dict()) ) def __getitem__(self, d): for k in self.keywords: if str(k).upper() == str(d).upper(): return self.keywords[k] for k in self.subsections: if str(k).upper() == str(d).upper(): return self.subsections[k] raise KeyError def __add__(self, other): if isinstance(other, (Keyword, KeywordList)): if other.name in self.keywords: self.keywords[other.name] += other else: self.keywords[other.name] = other elif isinstance(other, Section): self.insert(other) else: TypeError("Can only add sections or keywords.")
[docs] def add(self, other): """ Add another keyword to the current section """ assert isinstance(other, (Keyword, KeywordList)) self.__add__(other)
[docs] def get(self, d, default=None): """ Similar to get for dictionaries. This will attempt to retrieve the section or keyword matching d. Will not raise an error if d does not exist. Args: d: the key to retrieve, if present default: what to return if d is not found """ for k in self.keywords: if str(k).upper() == str(d).upper(): return self.keywords[k] for k in self.subsections: if str(k).upper() == str(d).upper(): return self.subsections[k] return default
def __setitem__(self, key, value): if isinstance(value, Section): if key in self.subsections: self.subsections[key] = value.__deepcopy__() else: self.insert(value) else: if not isinstance(value, (Keyword, KeywordList)): value = Keyword(key, value) match = [k for k in self.keywords if key.upper() == k.upper()] if match: del self.keywords[match[0]] self.keywords[key] = value def __delitem__(self, key): """ Delete section with name matching key OR delete all keywords with names matching this key """ l = [s for s in self.subsections if s.upper() == key.upper()] if l: del self.subsections[l[0]] return l = [k for k in self.keywords if k.upper() == key.upper()] if l: del self.keywords[l[0]] return raise KeyError("No section or keyword matching the given key.") def __sub__(self, other): return self.__delitem__(other)
[docs] def update(self, d: dict): """ Update the Section according to a dictionary argument. This is most useful for providing user-override settings to default parameters. As you pass a dictionary the class variables like "description", "location", or "repeats" are not included. Therefore, it is recommended that this be used to modify existing Section objects to a user's needs, but not used for the creation of new Section child-classes. Args: d (dict): A dictionary containing the update information. Should use nested dictionaries to specify the full path of the update. If a section or keyword does not exist, it will be created, but only with the values that are provided in "d", not using default values from a Section object. {'SUBSECTION1': {'SUBSEC2': {'NEW_KEYWORD', 'NEW_VAL'},{'NEW_SUBSEC': {'NEW_KWD': 'NEW_VAL'}}} """ Section._update(self, d)
@staticmethod def _update(d1, d2): """ Helper method for self.update(d) method (see above). """ for k, v in d2.items(): if isinstance(v, (str, float, int, bool)): d1[k] = Keyword(k, v) elif isinstance(v, (Keyword, KeywordList)): d1[k] = v elif isinstance(v, dict): tmp = [_ for _ in d1.subsections if k.upper() == _.upper()] if not tmp: d1.insert(Section(k, subsections={})) Section._update(d1.subsections[k], v) else: Section._update(d1.subsections[tmp[0]], v) elif isinstance(v, Section): d1.insert(v) else: print(type(v)) raise TypeError("Unrecognized type.")
[docs] def set(self, d: dict): """ Alias for update. Used by custodian. """ self.update(d)
[docs] def unset(self, d: dict): """ Dict based deletion. Used by custodian. """ for k, v in d.items(): if isinstance(v, (str, float, int, bool)): del self[k][v] elif isinstance(v, (Keyword, Section, KeywordList)): del self[k][v.name] elif isinstance(v, dict): self[k].unset(v) else: TypeError("Can only add sections or keywords.")
[docs] def inc(self, d: dict): """ Mongo style dict modification. Include. """ for k, v in d.items(): if isinstance(v, (str, float, bool, int)): v = Keyword(k, v) if isinstance(v, (Keyword, Section, KeywordList)): self.add(v) elif isinstance(v, dict): self[k].inc(v) else: TypeError("Can only add sections or keywords.")
[docs] def insert(self, d): """ Insert a new section as a subsection of the current one """ assert isinstance(d, Section) self.subsections[d.alias or d.name] = d.__deepcopy__()
[docs] def check(self, path: str): """ Check if section exists within the current using a path. Can be useful for cross-checking whether or not required dependencies have been satisfied, which CP2K does not enforce. Args: path (str): Path to section of form 'SUBSECTION1/SUBSECTION2/SUBSECTION_OF_INTEREST' """ _path = path.split("/") s = self.subsections for p in _path: tmp = [_ for _ in s if p.upper() == _.upper()] if tmp: s = s[tmp[0]].subsections else: return False return True
[docs] def by_path(self, path: str): """ Access a sub-section using a path. Used by the file parser. Args: path (str): Path to section of form 'SUBSECTION1/SUBSECTION2/SUBSECTION_OF_INTEREST' """ _path = path.split("/") if _path[0] == self.name: _path = _path[1:] s = self for p in _path: s = s.subsections[p] # only search subsections in case of repeat name return s
[docs] def get_string(self): """ Get string representation of Section """ return Section._get_string(self)
@staticmethod def _get_string(d, indent=0): """ Helper function to return a pretty string of the section. Includes indentation and descriptions (if present). """ string = "" if d.description and d.verbose: string += ( "\n" + textwrap.fill( d.description, initial_indent="\t" * indent + "! ", subsequent_indent="\t" * indent + "! ", width=50, ) + "\n" ) string += "\t" * indent + "&" + d.name string += " " + " ".join(map(str, d.section_parameters)) + "\n" for k, v in d.keywords.items(): if isinstance(v, KeywordList): string += v.get_string(indent=indent + 1) + "\n" else: string += "\t" * (indent + 1) + v.get_string() + "\n" for k, v in d.subsections.items(): string += v._get_string(v, indent + 1) string += "\t" * indent + "&END " + d.name + "\n" return string
[docs] def verbosity(self, verbosity): """ Change the section verbossity recursively by turning on/off the printing of descriptions. Turning off descriptions may reduce the appealing documentation of input files, but also helps de-clutter them. """ self.verbose = verbosity for k, v in self.keywords.items(): v.verbosity(verbosity) for k, v in self.subsections.items(): v.verbosity(verbosity)
[docs] def silence(self): """ Recursively delete all print sections so that only defaults are printed out. """ if self.subsections: if self.subsections.get("PRINT"): del self.subsections["PRINT"] for _s in self.subsections: self.subsections[_s].silence()
[docs]class Cp2kInput(Section): """ Special instance of 'Section' class that is meant to represent the overall cp2k input. Distinguishes itself from Section by overriding get_string() to not print this section's title and by implementing the file i/o """ def __init__(self, name: str = "CP2K_INPUT", subsections: dict = None, **kwargs): """ Initialize Cp2kInput by calling the super """ self.name = name self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "CP2K Input" super().__init__( name, repeats=False, description=description, section_parameters=[], subsections=subsections, **kwargs, )
[docs] def get_string(self): """ Get string representation of the Cp2kInput """ s = "" for k in self.subsections.keys(): s += self.subsections[k].get_string() return s
@classmethod def _from_dict(cls, d): """ Initialize from a dictionary """ return Cp2kInput( "CP2K_INPUT", subsections=getattr( __import__(d["@module"], globals(), locals(), d["@class"], 0), d["@class"], ) .from_dict(d) .subsections, )
[docs] @staticmethod def from_file(file: str): """ Initialize from a file """ with zopen(file, "rt") as f: txt = _preprocessor(f.read(), os.path.dirname(f.name)) return Cp2kInput.from_string(txt)
[docs] @staticmethod def from_string(s: str): """ Initialize from a string """ lines = s.splitlines() lines = [line.replace("\t", "") for line in lines] lines = [line.strip() for line in lines] lines = [line for line in lines if line] return Cp2kInput.from_lines(lines)
[docs] @classmethod def from_lines(cls, lines: Union[List, tuple]): """ Helper method to read lines of file """ cp2k_input = Cp2kInput("CP2K_INPUT", subsections={}) Cp2kInput._from_lines(cp2k_input, lines) return cp2k_input
def _from_lines(self, lines): """ Helper method, reads lines of text to get a Cp2kInput """ current = self.name description = "" for line in lines: if line.startswith("!") or line.startswith("#"): description += line[1:].strip() elif line.upper().startswith("&END"): current = "/".join(current.split("/")[:-1]) elif line.startswith("&"): name, subsection_params = line.split()[0][1:], line.split()[1:] alias = name + " " + " ".join(subsection_params) if subsection_params else None s = Section( name, section_parameters=subsection_params, alias=alias, subsections={}, description=description, ) description = "" self.by_path(current).insert(s) current = current + "/" + alias if alias else current + "/" + name else: kwd = Keyword.from_string(line) tmp = self.by_path(current).get(kwd.name) if tmp: if isinstance(tmp, KeywordList): self.by_path(current)[kwd.name].append(kwd) else: self.by_path(current)[kwd.name] = KeywordList(keywords=[kwd, tmp]) else: self.by_path(current).keywords[kwd.name] = kwd
[docs] def write_file( self, input_filename: str = "cp2k.inp", output_dir: str = ".", make_dir_if_not_present: bool = True, ): """ Write input to a file. """ if not os.path.isdir(output_dir) and make_dir_if_not_present: os.mkdir(output_dir) filepath = os.path.join(output_dir, input_filename) with open(filepath, "w") as f: f.write(self.get_string())
[docs]class Global(Section): """ Controls 'global' settings for cp2k execution such as RUN_TYPE and PROJECT_NAME """ def __init__(self, project_name: str = "CP2K", run_type: str = "ENERGY_FORCE", **kwargs): """ Initialize the global section """ self.project_name = project_name self.run_type = run_type self.kwargs = kwargs description = ( "Section with general information regarding which kind of simulation" + "to perform an parameters for the whole PROGRAM" ) keywords = { "PROJECT_NAME": Keyword("PROJECT_NAME", project_name), "RUN_TYPE": Keyword("RUN_TYPE", run_type), "EXTENDED_FFT_LENGTHS": Keyword("EXTENDED_FFT_LENGTHS", True), } super().__init__( "GLOBAL", description=description, keywords=keywords, subsections={}, **kwargs, )
[docs]class ForceEval(Section): """ Controls the calculation of energy and forces in Cp2k """ def __init__(self, subsections: dict = None, **kwargs): """ Initialize the ForceEval section """ self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "parameters needed to calculate energy and forces and describe the system you want to analyze." keywords = { "METHOD": Keyword("METHOD", kwargs.get("METHOD", "QS")), "STRESS_TENSOR": Keyword("STRESS_TENSOR", kwargs.get("STRESS_TENSOR", "ANALYTICAL")), } super().__init__( "FORCE_EVAL", repeats=True, description=description, keywords=keywords, subsections=subsections, **kwargs, )
[docs]class Dft(Section): """ Controls the DFT parameters in Cp2k """ def __init__( self, basis_set_filenames="BASIS_MOLOPT", potential_filename="GTH_POTENTIALS", uks: bool = True, wfn_restart_file_name=None, subsections: dict = None, **kwargs, ): """ Initialize the DFT section Args: subsections: Any subsections to initialize with basis_set_filename: Name of the file that contains the basis set information potential_filename: Name of the file that contains the pseudopotential information uks: Whether to run unrestricted Kohn Sham (spin polarized) """ self.basis_set_filenames = basis_set_filenames self.potential_filename = potential_filename self.uks = uks self.wfn_restart_file_name = wfn_restart_file_name self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "parameter needed by dft programs" keywords = { "BASIS_SET_FILE_NAME": KeywordList([Keyword("BASIS_SET_FILE_NAME", k) for k in basis_set_filenames]), "POTENTIAL_FILE_NAME": Keyword("POTENTIAL_FILE_NAME", potential_filename), "UKS": Keyword("UKS", uks), } if wfn_restart_file_name: keywords["WFN_RESTART_FILE_NAME"] = Keyword("WFN_RESTART_FILE_NAME", wfn_restart_file_name) super().__init__( "DFT", description=description, keywords=keywords, subsections=self.subsections, **kwargs, )
[docs]class Subsys(Section): """ Controls the definition of the system to be simulated """ def __init__(self, subsections: dict = None, **kwargs): """ Initialize the subsys section """ self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "a subsystem: coordinates, topology, molecules and cell" super().__init__("SUBSYS", description=description, subsections=subsections, **kwargs)
[docs]class QS(Section): """ Controls the quickstep settings (DFT driver) """ def __init__( self, method: str = "GPW", eps_default: float = 1e-7, extrapolation: str = "PS", subsections: dict = None, **kwargs, ): """ Initialize the QS Section Args: method: What dft methodology to use. Can be GPW (Gaussian Plane Waves) for DFT with pseudopotentials or GAPW (Gaussian Augmented Plane Waves) for all electron calculations eps_default: The default level of convergence accuracy. NOTE: This is a global value for all the numerical value of all EPS_* values in QS module. It is not the same as EPS_SCF, which sets convergence accuracy of the SCF cycle alone. extrapolation: Method use for extrapolation. If using gamma-point-only calculation, then one should use PS for relaxations and ASPC for MD. See the manual for other options. subsections: Subsections to initialize with """ self.method = method self.eps_default = eps_default self.extrapolation = extrapolation self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "parameters needed to set up the Quickstep framework" keywords = { "METHOD": Keyword("METHOD", method), "EPS_DEFAULT": Keyword("EPS_DEFAULT", eps_default), "EXTRAPOLATION": Keyword("EXTRAPOLATION", extrapolation), } super().__init__( "QS", description=description, keywords=keywords, subsections=subsections, **kwargs, )
[docs]class Scf(Section): """ Controls the self consistent field loop """ def __init__( self, max_scf: int = 50, eps_scf: float = 1e-6, scf_guess: str = "RESTART", subsections: dict = None, **kwargs, ): """ Initialize the Scf section Args: max_scf: maximum number of SCF loops before terminating eps_scf: convergence criteria for SCF loop scf_guess: Initial guess for SCF loop (RESTART will switch to ATOMIC when no restart file is present) """ self.max_scf = max_scf self.eps_scf = eps_scf self.scf_guess = scf_guess self.subsections = subsections if subsections else {} self.kwargs = kwargs description = "Parameters needed to perform an SCF run." keywords = { "MAX_SCF": Keyword("MAX_SCF", max_scf), "EPS_SCF": Keyword("EPS_SCF", eps_scf), "SCF_GUESS": Keyword("SCF_GUESS", scf_guess), # Uses Restart file if present, and ATOMIC if not present "MAX_ITER_LUMO": Keyword("MAX_ITER_LUMO", kwargs.get("max_iter_lumo", 400)), } super().__init__( "SCF", description=description, keywords=keywords, subsections=subsections, **kwargs, )
[docs]class Mgrid(Section): """ Controls the multigrid for numerical integration """ def __init__( self, cutoff: Union[int, float] = 1200, rel_cutoff: Union[int, float] = 80, ngrids: int = 5, progression_factor: int = 3, subsections: dict = None, **kwargs, ): """ Initialize the MGRID section Args: cutoff: Cutoff energy (in Rydbergs for historical reasons) defining how find of Gaussians will be used rel_cutoff: The relative cutoff energy, which defines how to map the Gaussians onto the multigrid. If the the value is too low then, even if you have a high cutoff with sharp Gaussians, they will be mapped to the course part of the multigrid ngrids: number of grids to use progression_factor: divisor that decides how to map Gaussians the multigrid after the highest mapping is decided by rel_cutoff """ self.cutoff = cutoff self.rel_cutoff = rel_cutoff self.ngrids = ngrids self.progression_factor = progression_factor self.subsections = subsections if subsections else {} self.kwargs = kwargs description = ( "Multigrid information. Multigrid allows for sharp gaussians and diffuse " + "gaussians to be treated on different grids, where the spacing of FFT integration " + "points can be tailored to the degree of sharpness/diffusiveness of the gaussians." ) keywords = { "CUTOFF": Keyword( "CUTOFF", cutoff, description="Cutoff in [Ry] for finest level of the MG.", ), "REL_CUTOFF": Keyword( "REL_CUTOFF", rel_cutoff, description="Controls which gaussians are mapped to which level of the MG", ), "NGRIDS": Keyword("NGRIDS", ngrids), "PROGRESSION_FACTOR": Keyword("PROGRESSION_FACTOR", progression_factor), } super().__init__( "MGRID", description=description, keywords=keywords, subsections=subsections, **kwargs, )
[docs]class Diagonalization(Section): """ Controls diagonalization settings (if using traditional diagonalization). """ def __init__( self, eps_adapt: float = 0, eps_iter: float = 1e-8, eps_jacobi: float = 0, jacobi_threshold: float = 1e-7, subsections: dict = None, **kwargs, ): """ Initialize the diagronalization section """ self.eps_adapt = eps_adapt self.eps_iter = eps_iter self.eps_jacobi = eps_jacobi self.jacobi_threshold = jacobi_threshold self.subsections = subsections if subsections else {} self.kwargs = kwargs location = "CP2K_INPUT/FORCE_EVAL/DFT/SCF/DIAGONALIZATION" keywords = { "EPS_ADAPT": Keyword("EPS_ADAPT", eps_adapt), "EPS_ITER": Keyword("EPS_ITER", eps_iter), "EPS_JACOBI": Keyword("EPS_JACOBI", eps_jacobi), "JACOBI_THRESHOLD": Keyword("JACOBI_THRESHOLD", jacobi_threshold), } super().__init__( "DIAGONALIZATION", keywords=keywords, repeats=False, location=location, subsections=self.subsections, **kwargs, )
[docs]class Davidson(Section): """ Parameters for davidson diagonalization """ def __init__( self, new_prec_each: int = 20, preconditioner: str = "FULL_SINGLE_INVERSE", **kwargs, ): """ Args: new_prec_each (int): How often to recalculate the preconditioner preconditioner (str): Preconditioner to use """ self.new_prec_each = new_prec_each self.preconditioner = preconditioner keywords = { "NEW_PREC_EACH": Keyword("NEW_PREC_EACH", new_prec_each), "PRECONDITIONER": Keyword("PRECONDITIONER", preconditioner), } super().__init__( "DAVIDSON", keywords=keywords, repeats=False, location=None, subsections={}, **kwargs, )
[docs]class OrbitalTransformation(Section): """ Turns on the Orbital Transformation scheme for diagonalizing the Hamiltonian. Much faster and with guaranteed convergence compared to normal diagonalization, but requires the system to have a band gap. NOTE: OT has poor convergence for metallic systems and cannot use SCF mixing or smearing. Therefore, you should not use it for metals or systems with 'small' band gaps. In that case, use normal diagonalization, which will be slower, but will converge properly. """ def __init__( self, minimizer: str = "CG", preconditioner: str = "FULL_ALL", algorithm: str = "STRICT", energy_gap: float = 0.01, linesearch: str = "2PNT", subsections: dict = None, **kwargs, ): """ Initialize the OT section Args: minimizer (str): The minimizer to use with the OT method. Default is conjugate gradient method, which is more robust, but more well-behaved systems should use DIIS, which can be as much as 50% faster. preconditioner (str): Preconditionar to use for OT, FULL_ALL tends to be most robust, but is not always most efficient. For difficult systems, FULL_SINGLE_INVERSE can be more robust, and is reasonably efficient with large systems. For huge, but well behaved, systems, where construction of the preconditioner can take a very long time, FULL_KINETIC can be a good choice. energy_gap (float): Guess for the band gap. For FULL_ALL, should be smaller than the actual band gap, so simply using 0.01 is a robust value. Choosing a larger value will help if you start with a bad initial guess though. For FULL_SINGLE_INVERSE, energy_gap is treated as a lower bound. Values lower than 0.05 in this case can lead to stability issues. algorithm (str): What algorithm to use for OT. 'Strict': Taylor or diagonalization based algorithm. IRAC: Orbital Transformation based Iterative Refinement of the Approximative Congruence transformation (OT/IR). linesearch (str): From the manual: 1D line search algorithm to be used with the OT minimizer, in increasing order of robustness and cost. MINIMIZER CG combined with LINESEARCH GOLD should always find an electronic minimum. Whereas the 2PNT minimizer is almost always OK, 3PNT might be needed for systems in which successive OT CG steps do not decrease the total energy. """ self.minimizer = minimizer self.preconditioner = preconditioner self.algorithm = algorithm self.energy_gap = energy_gap self.linesearch = linesearch self.subsections = subsections if subsections else {} self.kwargs = kwargs description = ( "Sets the various options for the orbital transformation (OT) method. " + "Default settings already provide an efficient, yet robust method. Most " + "systems benefit from using the FULL_ALL preconditioner combined with a small " + "value (0.001) of ENERGY_GAP. Well-behaved systems might benefit from using " + "a DIIS minimizer. Advantages: It's fast, because no expensive diagonalisation" + "is performed. If preconditioned correctly, method guaranteed to find minimum. " + "Disadvantages: Sensitive to preconditioning. A good preconditioner can be " + "expensive. No smearing, or advanced SCF mixing possible: POOR convergence for " + "metalic systems." ) keywords = { "MINIMIZER": Keyword("MINIMIZER", minimizer), "PRECONDITIONER": Keyword("PRECONDITIONER", preconditioner), "ENERGY_GAP": Keyword("ENERGY_GAP", energy_gap), "ALGORITHM": Keyword("ALGORITHM", algorithm), "LINESEARCH": Keyword("LINESEARCH", linesearch), } super().__init__( "OT", description=description, keywords=keywords, subsections=self.subsections, **kwargs, )
[docs]class Cell(Section): """ Defines the simulation cell (lattice) """ def __init__(self, lattice: Lattice, **kwargs): """ Initialize the cell section. Args: lattice: pymatgen lattice object """ self.lattice = lattice self.kwargs = kwargs description = "Input parameters needed to set up the CELL." keywords = { "A": Keyword("A", *lattice.matrix[0]), "B": Keyword("B", *lattice.matrix[1]), "C": Keyword("C", *lattice.matrix[2]), } super().__init__("CELL", description=description, keywords=keywords, subsections={}, **kwargs)
[docs]class Kind(Section): """ Specifies the information for the different atom types being simulated. """ def __init__( self, specie: str, alias: Union[str, None] = None, magnetization: float = 0.0, subsections: dict = None, basis_set: str = "GTH_BASIS", potential: str = "GTH_POTENTIALS", ghost: bool = False, **kwargs, ): """ Initialize a KIND section Args: specie (Species or Element): Object representing the atom. alias (str): Alias for the atom, can be used for specifying modifcations to certain atoms but not all, e.g. Mg_1 and Mg_2 to force difference oxidation states on the two atoms. magnetization (float): From the CP2K Manual: The magnetization used in the atomic initial guess. Adds magnetization/2 spin-alpha electrons and removes magnetization/2 spin-beta electrons. basis_set (str): Basis set for this atom, accessible from the basis set file specified potential (str): Pseudopotential for this atom, accessible from the potential file kwargs: Additional kwargs to pass to Section() """ self.name = "KIND" self.specie = specie self.alias = alias self.magnetization = magnetization self.subsections = subsections if subsections else {} self.basis_set = basis_set self.potential = potential self.ghost = ghost self.kwargs = kwargs self.description = "The description of the kind of the atoms (mostly for QM)" keywords = { "ELEMENT": Keyword("ELEMENT", specie.__str__()), "MAGNETIZATION": Keyword("MAGNETIZATION", magnetization), "BASIS_SET": Keyword("BASIS_SET", basis_set), "POTENTIAL": Keyword("POTENTIAL", potential), "GHOST": Keyword("GHOST", ghost), } kind_name = alias if alias else specie.__str__() self.alias = kind_name self.section_parameters = [kind_name] self.location = None self.verbose = True self.repeats = False super().__init__( name=self.name, subsections=self.subsections, description=self.description, keywords=keywords, section_parameters=self.section_parameters, alias=self.alias, location=self.location, verbose=self.verbose, **self.kwargs, )
[docs]class DftPlusU(Section): """ Controls DFT+U for an atom kind """ def __init__( self, eps_u_ramping=1e-5, init_u_ramping_each_scf=False, l=-1, u_minus_j=0, u_ramping=0, ): """ Initialize the DftPlusU section. Args: eps_u_ramping: (float) SCF convergence threshold at which to start ramping the U value init_u_ramping_each_scf: (bool) Whether or not to do u_ramping each scf cycle l: (int) angular moment of the orbital to apply the +U correction u_minus_j: (float) the effective U parameter, Ueff = U-J u_ramping: (float) stepwise amount to increase during ramping until u_minus_j is reached """ self.name = "DFT_PLUS_U" self.eps_u_ramping = 1e-5 self.init_u_ramping_each_scf = False self.l = l self.u_minus_j = u_minus_j self.u_ramping = u_ramping keywords = { "EPS_U_RAMPING": Keyword("EPS_U_RAMPING", eps_u_ramping), "INIT_U_RAMPING_EACH_SCF": Keyword("INIT_U_RAMPING_EACH_SCF", init_u_ramping_each_scf), "L": Keyword("L", l), "U_MINUS_J": Keyword("U_MINUS_J", u_minus_j), "U_RAMPING": Keyword("U_RAMPING", u_ramping), } super().__init__( name=self.name, subsections=None, description=self.description, keywords=keywords, section_parameters=self.section_parameters, alias=None, location=None, )
[docs]class Coord(Section): """ Specifies the coordinates of the atoms using a pymatgen structure object. """ def __init__( self, structure: Union[Structure, Molecule], aliases: Union[dict, None] = None, subsections: dict = None, **kwargs, ): """ Args: structure: Pymatgen structure object alias (bool): whether or not to identify the sites by Element + number so you can do things like assign unique magnetization do different elements. """ self.structure = structure self.aliases = aliases self.subsections = subsections if subsections else {} self.kwargs = kwargs description = ( "The coordinates for simple systems (like small QM cells) are specified " + "here by default using explicit XYZ coordinates. More complex systems " + "should be given via an external coordinate file in the SUBSYS%TOPOLOGY section." ) if aliases: keywords = {k: KeywordList([Keyword(k, *structure[i].coords) for i in aliases[k]]) for k in aliases} else: keywords = { ss: KeywordList([Keyword(s.specie.symbol, *s.coords) for s in structure.sites if s.specie.symbol == ss]) for ss in structure.symbol_set } super().__init__( name="COORD", description=description, keywords=keywords, alias=None, subsections=self.subsections, **kwargs, )
[docs]class PDOS(Section): """ Controls printing of projected density of states onto the different atom KINDS (elemental decomposed DOS). """ def __init__(self, nlumo: int = -1, **kwargs): """ Initialize the PDOS section Args: nlumo: how many unoccupied orbitals to include (-1==ALL) """ self.nlumo = nlumo self.kwargs = kwargs description = "Controls printing of the projected density of states" keywords = { "NLUMO": Keyword("NLUMO", nlumo), "COMPONENTS": Keyword("COMPONENTS"), } super().__init__("PDOS", description=description, keywords=keywords, subsections={}, **kwargs)
[docs]class LDOS(Section): """ Controls printing of the LDOS (List-Density of states). i.e. projects onto specific atoms. """ def __init__(self, index: int = 1, alias: Union[str, None] = None, **kwargs): """ Initialize the LDOS section Args: index: Index of the atom to project onto """ self.index = index self.alias = alias self.kwargs = kwargs description = "Controls printing of the projected density of states decomposed by atom type" keywords = {"COMPONENTS": Keyword("COMPONENTS"), "LIST": Keyword("LIST", index)} super().__init__( "LDOS", subsections={}, alias=alias, description=description, keywords=keywords, **kwargs, )
[docs]class V_Hartree_Cube(Section): """ Controls printing of the hartree potential as a cube file. """ def __init__(self, keywords=None, **kwargs): """ Initialize the V_HARTREE_CUBE section """ self.keywords = keywords if keywords else {} self.kwargs = kwargs description = ( "Controls the printing of a cube file with eletrostatic potential generated by " + "the total density (electrons+ions). It is valid only for QS with GPW formalism. " + "Note that by convention the potential has opposite sign than the expected physical one." ) super().__init__( "V_HARTREE_CUBE", subsections={}, description=description, keywords=keywords, **kwargs, )
[docs]class MO_Cubes(Section): """ Controls printing of the molecular orbital eigenvalues """ def __init__(self, write_cube: bool = False, nhomo: int = 1, nlumo: int = 1, **kwargs): """ Initialize the MO_CUBES section """ self.write_cube = write_cube self.nhomo = nhomo self.nlumo = nlumo self.kwargs = kwargs description = ( "Controls the printing of a cube file with eletrostatic potential generated by " + "the total density (electrons+ions). It is valid only for QS with GPW formalism. " + "Note that by convention the potential has opposite sign than the expected physical one." ) keywords = { "WRITE_CUBES": Keyword("WRITE_CUBE", write_cube), "NHOMO": Keyword("NHOMO", nhomo), "NLUMO": Keyword("NLUMO", nlumo), } super().__init__( "MO_CUBES", subsections={}, description=description, keywords=keywords, **kwargs, )
[docs]class E_Density_Cube(Section): """ Controls printing of the electron density cube file """ def __init__(self, **kwargs): """ Initialize the E_DENSITY_CUBE Section """ self.kwargs = kwargs description = ( "Controls the printing of cube files with the electronic density and, for LSD " + "calculations, the spin density." ) super().__init__( "E_DENSITY_CUBE", subsections={}, description=description, keywords={}, **kwargs, )
[docs]class Smear(Section): """ Control electron smearing """ def __init__( self, elec_temp: Union[int, float] = 300, method: str = "FERMI_DIRAC", fixed_magnetic_moment: float = -1e2, **kwargs, ): """ Initialize the SMEAR section """ self.elec_temp = elec_temp self.method = method self.fixed_magnetic_moment = fixed_magnetic_moment self.kwargs = kwargs description = "Activates smearing of electron occupations" keywords = { "ELEC_TEMP": Keyword("ELEC_TEMP", elec_temp), "METHOD": Keyword("METHOD", method), "FIXED_MAGNETIC_MOMENT": Keyword("FIXED_MAGNETIC_MOMENT", fixed_magnetic_moment), } super().__init__( "SMEAR", description=description, keywords=keywords, subsections={}, **kwargs, )
[docs]class BrokenSymmetry(Section): """ Define the required atomic orbital occupation assigned in initialization of the density matrix, by adding or subtracting electrons from specific angular momentum channels. It works only with GUESS ATOMIC """ def __init__( self, l_alpha: int = -1, n_alpha: int = 0, nel_alpha: int = -1, l_beta: int = -1, n_beta: int = 0, nel_beta: int = -1, ): """ Initialize the broken symmetry section Args: l_alpha: Angular momentum quantum number of the orbitals whose occupation is changed n_alpha: Principal quantum number of the orbitals whose occupation is changed. Default is the first not occupied nel_alpha: Orbital occupation change per angular momentum quantum number. In unrestricted calculations applied to spin alpha l_beta: Same as L_alpha for beta channel n_beta: Same as N_alpha for beta channel nel_beta: Same as NEL_alpha for beta channel """ self.l_alpha = l_alpha self.n_alpha = n_alpha self.nel_alpha = nel_alpha self.l_beta = l_beta self.n_beta = n_beta self.nel_beta = nel_beta description = ( "Define the required atomic orbital occupation assigned in initialization " + "of the density matrix, by adding or subtracting electrons from specific " + "angular momentum channels. It works only with GUESS ATOMIC" ) keywords_alpha = { "L": Keyword("L", l_alpha), "N": Keyword("N", n_alpha), "NEL": Keyword("NEL", nel_alpha), } alpha = Section("ALPHA", keywords=keywords_alpha, subsections={}, repeats=False) keywords_beta = { "L": Keyword("L", l_beta), "N": Keyword("N", n_beta), "NEL": Keyword("NEL", nel_beta), } beta = Section("BETA", keywords=keywords_beta, subsections={}, repeats=False) super().__init__( "BS", description=description, subsections={"ALPHA": alpha, "BETA": beta}, keywords={}, repeats=False, )
[docs]class XC_FUNCTIONAL(Section): """ Defines the XC functional to use """ def __init__(self, functional: str, subsections: dict = None, **kwargs): """ Initialize the XC_FUNCTIONAL class """ self.functional = functional self.subsections = subsections if subsections else {} self.kwargs = kwargs location = "CP2K_INPUT/FORCE_EVAL/DFT/XC/XC_FUNCTIONAL" built_in = [ "BL3YLP", "BEEFVDW", "BLYP", "BP", "HCTH120", "LDA", "NONE", "NO_SHORTCUT", "OLYP", "PADE", "PBE", "PBE0", "TPSS", ] if functional in built_in: section_params = [functional] elif functional.upper() in ["PBESOL", "REVPBE"]: section_params = ["PBE"] self.subsections["PBE"] = Section( "PBE", keywords={"PARAMETERIZATION": Keyword("PARAMETERIZATION", functional)}, ) else: section_params = [] warnings.warn( "The selected functional has no short-cut in CP2K. " "You must specify subsection to define this functional." ) super().__init__( "XC_FUNCTIONAL", subsections=self.subsections, location=location, repeats=False, section_parameters=section_params, **kwargs, )
[docs]class PBE(Section): """ Info about the PBE functional. """ def __init__( self, parameterization: str = "ORIG", scale_c: Union[float, int] = 1, scale_x: Union[float, int] = 1, ): """ Args: parameterization (str): ORIG: original PBE PBESOL: PBE for solids/surfaces REVPBE: revised PBE scale_c (float): scales the correlation part of the functional. scale_x (float): scales the exchange part of the functional. """ self.parameterization = parameterization self.scale_c = scale_c self.scale_x = scale_x location = "CP2K_INPUT/FORCE_EVAL/DFT/XC/XC_FUNCTIONAL/PBE" keywords = { "PARAMETRIZATION": Keyword("PARAMETRIZATION", parameterization), "SCALE_C": Keyword("SCALE_C", scale_c), "SCALE_X": Keyword("SCALE_X", scale_x), } super().__init__( "PBE", subsections={}, repeats=False, location=location, section_parameters=[], keywords=keywords, )
[docs]class Kpoints(Section): """ Description of the k-points to use for the calculation. """ def __init__( self, kpts: Union[Sequence, Sequence[Sequence[int]]], weights: Union[Sequence, None] = None, eps_geo: float = 1e-6, full_grid: bool = False, parallel_group_size: int = -1, scheme: str = "MONKHORST-PACK", symmetry: bool = False, units: str = "B_VECTOR", verbose: bool = False, wavefunctions: str = "COMPLEX", ): """ Args: kpts (list, tuple): a 2D array for the kpoints of the form [(1,1,1),]. If len(kpts) == 1. Then it is taken as subdivisions for automatic kpoint scheme. If it has more entries, it is taken as manual entries for kpoints. weights (list, tuple): a weight for each kpoint. Default is to weigh each by 1 eps_geo (float): tolerance for symmetry. Default=1e-6 full_grid (bool): use full (not reduced) kpoint grid. Default=False. parallel_group_size (int): from cp2k manual: Number of processors to be used for a single kpoint. This number must divide the total number of processes. The number of groups must divide the total number of kpoints. Value=-1 (smallest possible number of processes per group, satisfying the constraints). Value=0 (all processes). Value=n (exactly n processes). Default=-1. scheme (str): kpoint generation scheme. Default='Monkhorst-Pack' symmetry (bool): Use symmetry to reduce the number of kpoints. Default=False. units (str): Units for the kpoint coordinates (reciprocal coordinates or cartesian). Default='B_VECTOR' (reciprocal) verbose (bool): verbose output for kpoints. Default=False wavefunctions (str): Whether to use complex or real valued wavefunctions (if available). Default='complex' """ description = "Sets up the kpoints" keywords = {} self.kpts = kpts self.weights = weights if weights else [1] * len(kpts) assert len(self.kpts) == len(self.weights) self.eps_geo = eps_geo self.full_grid = full_grid self.parallel_group_size = parallel_group_size self.scheme = scheme self.symmetry = symmetry self.units = units self.verbose = verbose self.wavefunctions = wavefunctions if len(kpts) == 1: keywords["SCHEME"] = Keyword("SCHEME", scheme, *kpts[0]) elif len(kpts) > 1: keywords["KPOINT"] = KeywordList([Keyword("KPOINT", *k, w) for k, w in zip(self.kpts, self.weights)]) else: raise ValueError("No k-points provided!") keywords.update( { "EPS_GEO": Keyword("EPS_GEO", eps_geo), "FULL_GRID": Keyword("FULL_GRID", full_grid), "PARALLEL_GROUP_SIZE": Keyword("PARALLEL_GROUP_SIZE", parallel_group_size), "SYMMETRY": Keyword("SYMMETRY", symmetry), "UNITS": Keyword("UNITS", units), "VERBOSE": Keyword("VERBOSE", verbose), "WAVEFUNCTIONS": Keyword("WAVEFUNCTIONS", wavefunctions), } ) super().__init__( name="KPOINTS", subsections=None, repeats=False, description=description, keywords=keywords, )
[docs] @classmethod def from_kpoints(cls, kpoints): """ Initialize the section from a Kpoints object (pymatgen.io.vasp.inputs). Args: kpoints: A pymatgen kpoints object. """ k = kpoints.as_dict() kpoints = k["kpoints"] weights = k["kpts_weights"] scheme = k["generation_style"] if scheme.lower() == "Monkhorst": scheme = "MONKHORST-PACK" units = k["coord_type"] if k["coord_type"]: if k["coord_type"].lower() == "reciprocal": units = "B_VECTOR" elif k["coord_type"].lower() == "cartesian": units = "CART_ANGSTROM" else: units = "B_VECTOR" return Kpoints(kpts=kpoints, weights=weights, scheme=scheme, units=units)