"""Structure optimization. """ import time from math import sqrt from os.path import isfile from ase.io.jsonio import read_json, write_json from ase.calculators.calculator import PropertyNotImplementedError from ase.parallel import world, barrier from ase.io.trajectory import Trajectory from ase.utils import IOContext import collections.abc class RestartError(RuntimeError): pass class Dynamics(IOContext): """Base-class for all MD and structure optimization classes.""" def __init__( self, atoms, logfile, trajectory, append_trajectory=False, master=None ): """Dynamics object. Parameters: atoms: Atoms object The Atoms object to operate on. logfile: file object or str If *logfile* is a string, a file with that name will be opened. Use '-' for stdout. trajectory: Trajectory object or str Attach trajectory object. If *trajectory* is a string a Trajectory will be constructed. Use *None* for no trajectory. append_trajectory: boolean Defaults to False, which causes the trajectory file to be overwriten each time the dynamics is restarted from scratch. If True, the new structures are appended to the trajectory file instead. master: boolean Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files. """ self.atoms = atoms self.logfile = self.openfile(logfile, mode='a', comm=world) self.observers = [] self.nsteps = 0 # maximum number of steps placeholder with maxint self.max_steps = 100000000 if trajectory is not None: if isinstance(trajectory, str): mode = "a" if append_trajectory else "w" trajectory = self.closelater(Trajectory( trajectory, mode=mode, atoms=atoms, master=master )) self.attach(trajectory) def get_number_of_steps(self): return self.nsteps def insert_observer( self, function, position=0, interval=1, *args, **kwargs ): """Insert an observer.""" if not isinstance(function, collections.abc.Callable): function = function.write self.observers.insert(position, (function, interval, args, kwargs)) def attach(self, function, interval=1, *args, **kwargs): """Attach callback function. If *interval > 0*, at every *interval* steps, call *function* with arguments *args* and keyword arguments *kwargs*. If *interval <= 0*, after step *interval*, call *function* with arguments *args* and keyword arguments *kwargs*. This is currently zero indexed.""" if hasattr(function, "set_description"): d = self.todict() d.update(interval=interval) function.set_description(d) if not hasattr(function, "__call__"): function = function.write self.observers.append((function, interval, args, kwargs)) def call_observers(self): for function, interval, args, kwargs in self.observers: call = False # Call every interval iterations if interval > 0: if (self.nsteps % interval) == 0: call = True # Call only on iteration interval elif interval <= 0: if self.nsteps == abs(interval): call = True if call: function(*args, **kwargs) def irun(self): """Run dynamics algorithm as generator. This allows, e.g., to easily run two optimizers or MD thermostats at the same time. Examples: >>> opt1 = BFGS(atoms) >>> opt2 = BFGS(StrainFilter(atoms)).irun() >>> for _ in opt2: >>> opt1.run() """ # compute initial structure and log the first step self.atoms.get_forces() # yield the first time to inspect before logging yield False if self.nsteps == 0: self.log() self.call_observers() # run the algorithm until converged or max_steps reached while not self.converged() and self.nsteps < self.max_steps: # compute the next step self.step() self.nsteps += 1 # let the user inspect the step and change things before logging # and predicting the next step yield False # log the step self.log() self.call_observers() # finally check if algorithm was converged yield self.converged() def run(self): """Run dynamics algorithm. This method will return when the forces on all individual atoms are less than *fmax* or when the number of steps exceeds *steps*.""" for converged in Dynamics.irun(self): pass return converged def converged(self, *args): """" a dummy function as placeholder for a real criterion, e.g. in Optimizer """ return False def log(self, *args): """ a dummy function as placeholder for a real logger, e.g. in Optimizer """ return True def step(self): """this needs to be implemented by subclasses""" raise RuntimeError("step not implemented.") class Optimizer(Dynamics): """Base-class for all structure optimization classes.""" # default maxstep for all optimizers defaults = {'maxstep': 0.2} def __init__( self, atoms, restart, logfile, trajectory, master=None, append_trajectory=False, force_consistent=False, ): """Structure optimizer object. Parameters: atoms: Atoms object The Atoms object to relax. restart: str Filename for restart file. Default value is *None*. logfile: file object or str If *logfile* is a string, a file with that name will be opened. Use '-' for stdout. trajectory: Trajectory object or str Attach trajectory object. If *trajectory* is a string a Trajectory will be constructed. Use *None* for no trajectory. master: boolean Defaults to None, which causes only rank 0 to save files. If set to true, this rank will save files. append_trajectory: boolean Appended to the trajectory file instead of overwriting it. force_consistent: boolean or None Use force-consistent energy calls (as opposed to the energy extrapolated to 0 K). If force_consistent=None, uses force-consistent energies if available in the calculator, but falls back to force_consistent=False if not. """ Dynamics.__init__( self, atoms, logfile, trajectory, append_trajectory=append_trajectory, master=master, ) self.force_consistent = force_consistent if self.force_consistent is None: self.set_force_consistent() self.restart = restart # initialize attribute self.fmax = None if restart is None or not isfile(restart): self.initialize() else: self.read() barrier() def todict(self): description = { "type": "optimization", "optimizer": self.__class__.__name__, } return description def initialize(self): pass def irun(self, fmax=0.05, steps=None): """ call Dynamics.irun and keep track of fmax""" self.fmax = fmax if steps: self.max_steps = steps return Dynamics.irun(self) def run(self, fmax=0.05, steps=None): """ call Dynamics.run and keep track of fmax""" self.fmax = fmax if steps: self.max_steps = steps return Dynamics.run(self) def converged(self, forces=None): """Did the optimization converge?""" if forces is None: forces = self.atoms.get_forces() if hasattr(self.atoms, "get_curvature"): return (forces ** 2).sum( axis=1 ).max() < self.fmax ** 2 and self.atoms.get_curvature() < 0.0 return (forces ** 2).sum(axis=1).max() < self.fmax ** 2 def log(self, forces=None): if forces is None: forces = self.atoms.get_forces() fmax = sqrt((forces ** 2).sum(axis=1).max()) e = self.atoms.get_potential_energy( force_consistent=self.force_consistent ) T = time.localtime() if self.logfile is not None: name = self.__class__.__name__ if self.nsteps == 0: args = (" " * len(name), "Step", "Time", "Energy", "fmax") msg = "%s %4s %8s %15s %12s\n" % args self.logfile.write(msg) if self.force_consistent: msg = "*Force-consistent energies used in optimization.\n" self.logfile.write(msg) ast = {1: "*", 0: ""}[self.force_consistent] args = (name, self.nsteps, T[3], T[4], T[5], e, ast, fmax) msg = "%s: %3d %02d:%02d:%02d %15.6f%1s %12.4f\n" % args self.logfile.write(msg) self.logfile.flush() def dump(self, data): if world.rank == 0 and self.restart is not None: with open(self.restart, 'w') as fd: write_json(fd, data) def load(self): with open(self.restart) as fd: try: return read_json(fd, always_array=False) except Exception as ex: msg = ('Could not decode restart file as JSON. ' f'You may need to delete the restart file {self.restart}') raise RestartError(msg) from ex def set_force_consistent(self): """Automatically sets force_consistent to True if force_consistent energies are supported by calculator; else False.""" try: self.atoms.get_potential_energy(force_consistent=True) except PropertyNotImplementedError: self.force_consistent = False else: self.force_consistent = True