gofee.py 15.4 KB
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import numpy as np
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import pickle
from os.path import isfile
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from surrogate.gpr import GPR
from population import population

from ase import Atoms
from ase.io import read, write, Trajectory
from ase.calculators.singlepoint import SinglePointCalculator
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from ase.calculators.calculator import FileIOCalculator
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from bfgslinesearch_zlim import BFGSLineSearch_zlim
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from bfgslinesearch_constrained import BFGSLineSearch_constrained
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from ase.ga.relax_attaches import VariansBreak
from parallel_utils import split, parallel_function_eval
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from candidate_operations.candidate_generation import OperationSelector
from candidate_operations.basic_mutations import RattleMutation


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from mpi4py import MPI
world = MPI.COMM_WORLD

import traceback
import sys


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def relax(structure, calc, Fmax=0.05, steps_max=200, dmax_cov=None):
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    a = structure.copy()
    # Set calculator 
    a.set_calculator(calc)
    pos_init = a.get_positions()

    # Catch if linesearch fails
    try:
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        dyn = BFGSLineSearch_constrained(a,
                                         logfile=None,
                                         pos_init=pos_init,
                                         dmax_cov=dmax_cov)
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        dyn.run(fmax = Fmax, steps = steps_max)
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    except Exception as err:
        print('Error in surrogate-relaxation:', err, flush=True)
        traceback.print_exc()
        traceback.print_exc(file=sys.stderr)
    return a


class GOFEE():
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    """GOFEE global structure search method.
        
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    structures: Atoms-object, list of Atoms-objects or None.
    In initial structures from which to start the sesarch.
    If None, the startgenerator must be supplied.
    If less than Ninit structures is supplied, the remaining
    ones are generated using the startgenerator or by rattling
    the supplied structures, depending on wether the
    startgenerator is supplied.

    gpr: The Gaussian Process Regression model used as the
    surrogate model for the Potential energy surface.
    
    startgenerator: 

    trajectory:

    kappa: 
    
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    """
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    def __init__(self, structures=None,
                 calc=None,
                 gpr=None,
                 startgenerator=None,
                 candidate_generator=None,
                 trajectory=None,
                 kappa=2,
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                 Niter=200,
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                 Ninit=10,
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                 dmax_cov=3.5,
                 Ncandidates=30,
                 population_size=5,
                 dualpoint=True,
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                 min_certainty=0.7,
                 restart=None):
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        if structures is None:
            assert startgenerator is not None
            self.structures = None
        else:
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            if isinstance(structures, Atoms):
                self.structures = [structures]
            elif isinstance(structures, list):
                assert isinstance(structures[0], Atoms)
                self.structures = structures
            elif isinstance(structures, str):
                self.structures = read(structures, index=':')
        
        if calc is not None:
            self.calc = calc
        else:
            assert structures is not None
            calc = structures[0].get_calculator()
            assert calc is not None and not isinstance(calc, SinglePointCalculator)
            print('Using calculator from supplied structure(s)')
            self.calc = calc

        if gpr is not None:
            self.gpr = gpr
        else:
            self.gpr = GPR()

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        if startgenerator is None:
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            assert structures is not None
            self.startgenerator = None
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        else:
            self.startgenerator = startgenerator
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        if startgenerator is not None:
            self.n_to_optimize = len(self.startgenerator.stoichiometry)
        else:
            self.n_to_optimize = len(self.structures[0])
            for constraint in self.structures[0].constraints:
                if isinstance(constraint, FixAtoms):
                    indices_fixed = constraint.get_indices()
                    self.n_to_optimize -= len(indices_fixed)
                    break
            
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        if candidate_generator is not None:
            self.candidate_generator = candidate_generator
        else:
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            rattle = RattleMutation(self.n_to_optimize,
                                    Nrattle=3,
                                    rattle_range=4)
            self.candidate_generator = OperationSelector([1.0],[rattle])
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        # Initialize population
        self.population = population(population_size=population_size, gpr=self.gpr, similarity2equal=0.9999)

        # Define parallel communication
        self.comm = world.Dup()  # Important to avoid mpi-problems from call to ase.parallel in BFGS
        self.master = self.comm.rank == 0

        self.kappa = kappa
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        self.Niter = Niter
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        self.Ninit = Ninit
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        self.dmax_cov = dmax_cov
        self.Ncandidates = Ncandidates
        self.dualpoint = dualpoint
        self.min_certainty = min_certainty
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        self.restart = restart
        
        if isinstance(trajectory, str):
            self.trajectory = Trajectory(filename=trajectory, mode='a', master=self.master)
            if self.restart:
                self.traj_name = trajectory
        elif isinstance(trajectory, Trajectory):
            self.trajectory = trajectory
        else:
            assert trajectory is None
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        if restart is None or not isfile(restart):
            self.initialize()
        else:
            self.read()
            self.comm.barrier()

    def initialize(self):
        self.steps = 0
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    def get_initial_structures(self):
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        """Method to prepare the initial structures for the search.
        
        The method makes sure that there are atleast self.Ninit
        initial structures.
        These structures are first of all the potentially supplied
        structures. If more structures are required, these are
        generated using self.startgenerator (if supplied), otherwise
        they are generated by heavily rattling the supplied structures.
        """
        # Collect potentially supplied structures and evaluate
        # energies and forces if not present.
        structures_init = []
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        if self.structures is not None:
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            for a in self.structures:
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                calc = a.get_calculator()
                if isinstance(calc, SinglePointCalculator):
                    if 'energy' in calc.results and 'forces' in calc.results:
                        # Add without evaluating.
                        structures_init.append(a)
                        self.write(a)
                        continue
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                a = self.evaluate(a)
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                structures_init.append(a)

        Nremaining = self.Ninit - len(structures_init)
    
        if Nremaining > 0 and self.startgenerator is None:
            # Initialize rattle-mutation for all atoms.
            rattle = RattleMutation(self.n_to_optimize,
                                    Nrattle=self.n_to_optimize,
                                    rattle_range=2)

        # Generation of remaining initial-structures (up to self.Ninit).
        for i in range(Nremaining):
            if self.startgenerator is not None:
                a = self.startgenerator.get_new_candidate()
            else:
                # Perform two times rattle of all atoms.
                a0 = structures_init[i % len(structures_init)]
                a = rattle.get_new_candidate([a])
                a = rattle.get_new_candidate([a])
            a = self.evaluate(a)
            structures_init.append(a)

        # Potentially do a few relaxation steps.
        ### missing code ###
            
        self.gpr.memory.save_data(structures_init)
        self.population.add(structures_init)
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    def run(self):
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        self.get_initial_structures()
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        while self.steps < self.Niter:
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            self.print_master('steps:', self.steps)
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            self.train_surrogate()
            self.update_population()
            relaxed_candidates = self.get_surrogate_relaxed_candidates()

            kappa = self.kappa
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            a_add = []
            for _ in range(5):
                try:
                    anew = self.select_with_acquisition(relaxed_candidates, kappa)
                    self.print_master('aq done')
                    anew = self.evaluate(anew)
                    a_add.append(anew)
                    self.print_master('sp done')
                    if self.dualpoint:
                        adp = self.get_dualpoint(anew)
                        adp = self.evaluate(adp)
                        a_add.append(adp)
                    self.print_master('dp done')
                    break
                except Exception as err:
                    kappa /=2
                    if self.master:
                        traceback.print_exc(file=sys.stderr)
            self.gpr.memory.save_data(a_add)
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            self.steps += 1
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            # Add structure to population
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            self.population.add(a_add)
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            # Save search state
            self.dump((self.steps, self.population, np.random.get_state()))
            
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            if self.master:
                print('anew pred:', anew.info['key_value_pairs']['Epred'], anew.info['key_value_pairs']['Epred_std'])
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                print('E_true:', [a.get_potential_energy() for a in a_add])
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                print('pop:', [a.get_potential_energy() for a in self.population.pop])
            
    def get_dualpoint(self, a, lmax=0.10, Fmax_flat=5):
        """Returns dual-point structure, i.e. the original structure
        perturbed slightly along the forces.
        
        lmax: The atom with the largest force will be displaced by
        this distance
        
        Fmax_flat: maximum atomic displacement. is increased linearely
        with force until Fmax = Fmax_flat, after which it remains
        constant as lmax.
        """
        F = a.get_forces()
        a_dp = a.copy()

        # Calculate and set new positions
        Fmax = np.sqrt((F**2).sum(axis=1).max())
        pos_displace = lmax * F*min(1/Fmax_flat, 1/Fmax)
        pos_dp = a.positions + pos_displace
        a_dp.set_positions(pos_dp)
        return a_dp

    def print_master(self, *args):
        self.comm.barrier()
        if self.master:
            print(*args, flush=True)

    def generate_candidate(self):
        Ntrials = 5
        for i_trial in range(Ntrials):
            parents = self.population.get_structure_pair()
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            a_mutated = self.candidate_generator.get_new_candidate(parents)
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            # break trial loop if successful
            if a_mutated is not None:
                break
            # If no success in max number of trials
        if a_mutated is None:
            a_mutated = parents[0].copy()
        return a_mutated

    def get_surrogate_relaxed_candidates(self):
        Njobs = self.Ncandidates
        task_split = split(Njobs, self.comm.size)
        def func1():
            return [self.generate_candidate() for i in task_split[self.comm.rank]]
        candidates = parallel_function_eval(self.comm, func1)
        
        def func2():
            return [self.surrogate_relaxation(candidates[i], Fmax=0.1, steps=200, kappa=self.kappa)
                    for i in task_split[self.comm.rank]]
        relaxed_candidates = parallel_function_eval(self.comm, func2)
        relaxed_candidates = self.certainty_filter(relaxed_candidates)

        #if (self.NsearchIter % self.Nuse_pop_as_candidates) == 0:
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        if self.steps % 3 == 0:
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            relaxed_candidates = self.population.pop_MLrelaxed + relaxed_candidates
        
        if self.master:
            Epred = np.array([a.info['key_value_pairs']['Epred'] for a in relaxed_candidates])
            Epred_std = np.array([a.info['key_value_pairs']['Epred_std'] for a in relaxed_candidates])
            fitness = Epred - self.kappa*Epred_std
            print(np.c_[Epred, Epred_std, fitness])
        return relaxed_candidates
        
    def certainty_filter(self, structures):
        certainty = np.array([a.info['key_value_pairs']['Epred_std']
                              for a in structures]) / np.sqrt(self.gpr.K0)
        min_certainty = self.min_certainty
        for _ in range(5):
            filt = certainty < min_certainty
            if np.sum(filt.astype(int)) > 0:
                structures = [structures[i] for i in range(len(filt)) if filt[i]]
                break
            else:
                min_certainty = min_certainty + (1-min_certainty)/2
        return structures

    def update_population(self):
        Njobs = len(self.population.pop)
        task_split = split(Njobs, self.comm.size)
        func = lambda: [self.surrogate_relaxation(self.population.pop[i],
                                                  Fmax=0.001, steps=200, kappa=None)
                        for i in task_split[self.comm.rank]]
        self.population.pop_MLrelaxed = parallel_function_eval(self.comm, func)
        if self.master:
            print('ML-relaxed pop forces:\n',
                  [(a.get_forces()**2).sum(axis=1).max()**0.5 for a in self.population.pop_MLrelaxed])

    def train_surrogate(self):
        # Train
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        if self.steps < 50 or (self.steps % 10) == 0:
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            self.gpr.optimize_hyperparameters(comm=self.comm)
        else:
            self.gpr.train()
        if self.master:
            print('kernel:', list(np.exp(self.gpr.kernel.theta)))
            print('lml:', self.gpr.lml)

    def surrogate_relaxation(self, a, Fmax=0.1, steps=200, kappa=None):
        calc = self.gpr.get_calculator(kappa)
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        a_relaxed = relax(a, calc, dmax_cov=self.dmax_cov, Fmax=Fmax, steps_max=steps)
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        # Evaluate uncertainty
        E, Estd = self.gpr.predict_energy(a_relaxed, eval_std=True)
        a_relaxed.info['key_value_pairs']['Epred'] = E
        a_relaxed.info['key_value_pairs']['Epred_std'] = Estd

        return a_relaxed

    def select_with_acquisition(self, structures, kappa):
        Epred = np.array([a.info['key_value_pairs']['Epred']
                          for a in structures])
        Epred_std = np.array([a.info['key_value_pairs']['Epred_std']
                              for a in structures])
        acquisition = Epred - kappa*Epred_std
        index_select = np.argmin(acquisition)
        return structures[index_select]

    def evaluate(self, a):
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        a = self.comm.bcast(a, root=0)
        
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        if isinstance(self.calc, FileIOCalculator):
            if self.master:
                a.set_calculator(self.calc)
                E = a.get_potential_energy()
                F = a.get_forces()
                results = {'energy': E, 'forces': F}
                calc_sp = SinglePointCalculator(a, **results)
                a.set_calculator(calc_sp)
            a = self.comm.bcast(a, root=0)
        else:
            a.set_calculator(self.calc)
            E = a.get_potential_energy()
            F = a.get_forces()
            results = {'energy': E, 'forces': F}
            calc_sp = SinglePointCalculator(a, **results)
            a.set_calculator(calc_sp)
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        self.write(a)

        return a

    def write(self, a):
        if self.trajectory is not None:
            self.trajectory.write(a)

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    def dump(self, data):
        if self.comm.rank == 0 and self.restart is not None:
            pickle.dump(data, open(self.restart, "wb"), protocol=2)
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    def read(self):
        self.steps, self.population, random_state = pickle.load(open(self.restart, "rb"))
        np.random.set_state(random_state)
        training_structures = read(self.traj_name, index=':')
        self.gpr.memory.save_data(training_structures)