mlip_arena/tasks/elasticity.py

"""
Defines the tasks for computing the elastic tensor.

This module has been modified from MatCalc
https://github.com/materialsvirtuallab/matcalc/blob/main/src/matcalc/elasticity.py

https://github.com/materialsvirtuallab/matcalc/blob/main/LICENSE

BSD 3-Clause License

Copyright (c) 2023, Materials Virtual Lab

Redistribution and use in source and binary forms, with or without
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any

import numpy as np
from ase import Atoms
from ase.calculators.calculator import BaseCalculator
from ase.optimize.optimize import Optimizer
from numpy.typing import ArrayLike
from prefect import task
from prefect.cache_policies import INPUTS, TASK_SOURCE
from prefect.runtime import task_run
from prefect.states import State

from mlip_arena.tasks.optimize import run as OPT
from pymatgen.analysis.elasticity import DeformedStructureSet, ElasticTensor, Strain
from pymatgen.analysis.elasticity.elastic import get_strain_state_dict
from pymatgen.io.ase import AseAtomsAdaptor

if TYPE_CHECKING:
    from ase.filters import Filter


def _generate_task_run_name():
    task_name = task_run.task_name
    parameters = task_run.parameters

    atoms = parameters["atoms"]
    calculator_name = parameters["calculator"]

    return f"{task_name}: {atoms.get_chemical_formula()} - {calculator_name}"


@task(
    name="Elasticity",
    task_run_name=_generate_task_run_name,
    cache_policy=TASK_SOURCE + INPUTS,
    # cache_key_fn=task_input_hash,
)
def run(
    atoms: Atoms,
    calculator: BaseCalculator,
    optimizer: Optimizer | str = "BFGSLineSearch",  # type: ignore
    optimizer_kwargs: dict | None = None,
    filter: Filter | str | None = "FrechetCell",  # type: ignore
    filter_kwargs: dict | None = None,
    criterion: dict | None = None,
    normal_strains: list[float] | np.ndarray | None = np.linspace(-0.01, 0.01, 4),
    shear_strains: list[float] | np.ndarray | None = np.linspace(-0.06, 0.06, 4),
    persist_opt: bool = True,
    cache_opt: bool = False,
) -> dict[str, Any] | State:
    """
    Compute the elastic tensor for the given structure and calculator.

    Args:
        atoms (Atoms): The input structure.
        calculator (BaseCalculator): The calculator.
        optimizer (Optimizer | str, optional): The optimizer. Defaults to "BFGSLineSearch".
        optimizer_kwargs (dict, optional): The optimizer kwargs. Defaults to None.
        filter (Filter | str, optional): The filter. Defaults to "FrechetCell".
        filter_kwargs (dict, optional): The filter kwargs. Defaults to None.
        criterion (dict, optional): The criterion. Defaults to None.
        normal_strains (list[float] | np.ndarray, optional): The normal strains. Defaults to np.linspace(-0.01, 0.01, 4).
        shear_strains (list[float] | np.ndarray, optional): The shear strains. Defaults to np.linspace(-0.06, 0.06, 4).
        concurrent (bool, optional): Whether to run concurrently. Defaults to True.
        persist_opt (bool, optional): Whether to persist the optimizer results. Defaults to True.
        cache_opt (bool, optional): Whether to cache the optimizer results. Defaults to True.

    Returns:
        dict[str, Any] | State: The elastic tensor.
    """

    atoms = atoms.copy()

    OPT_ = OPT.with_options(
        refresh_cache=not cache_opt,
        persist_result=persist_opt,
    )

    first_relax = OPT_(
        atoms=atoms,
        calculator=calculator,
        optimizer=optimizer,
        optimizer_kwargs=optimizer_kwargs,
        filter=filter,
        filter_kwargs=filter_kwargs,
        criterion=criterion,
        return_state=True,
    )

    if first_relax.is_failed():
        return first_relax

    result = first_relax.result(raise_on_failure=False)

    assert isinstance(result, dict)
    relaxed = result["atoms"]

    if isinstance(normal_strains, np.ndarray):
        normal_strains = normal_strains.tolist()
    if isinstance(shear_strains, np.ndarray):
        shear_strains = shear_strains.tolist()

    assert isinstance(relaxed, Atoms)
    assert isinstance(normal_strains, list)
    assert isinstance(shear_strains, list)

    structure = AseAtomsAdaptor.get_structure(relaxed)  # type: ignore

    deformed_structure_set = DeformedStructureSet(
        structure,
        normal_strains,
        shear_strains,
    )

    stresses = []
    for deformed_structure in deformed_structure_set:
        atoms = deformed_structure.to_ase_atoms()
        atoms.calc = relaxed.calc
        stresses.append(atoms.get_stress(voigt=False))

    strains = [
        Strain.from_deformation(deformation)
        for deformation in deformed_structure_set.deformations
    ]

    fit = fit_elastic_tensor(
        strains, stresses, eq_stress=relaxed.get_stress(voigt=False)
    )

    return {
        "elastic_tensor": fit["elastic_tensor"],
        "residuals_sum": fit["residuals_sum"],
    }


@task
def fit_elastic_tensor(
    strains: ArrayLike,
    stresses: ArrayLike,
    eq_stress: ArrayLike | None = None,
    tolerance: float = 1e-7,
):
    """
    Compute the elastic tensor from the given strains and stresses.

    Args:
        strains (ArrayLike): The strains.
        stresses (ArrayLike): The stresses.
        tolerance (float, optional): The tolerance. Defaults to 1e-7.

    Returns:
        ElasticTensor: The elastic tensor.
    """

    strain_states = [tuple(ss) for ss in np.eye(6)]
    ss_dict = get_strain_state_dict(
        strains,
        stresses,
        eq_stress=eq_stress,
        add_eq=True if eq_stress is not None else False,
    )
    c_ij = np.zeros((6, 6))
    residuals_sum = 0.0
    for ii in range(6):
        strain = ss_dict[strain_states[ii]]["strains"]
        stress = ss_dict[strain_states[ii]]["stresses"]
        for jj in range(6):
            fit = np.polyfit(strain[:, ii], stress[:, jj], 1, full=True)
            c_ij[ii, jj] = fit[0][0]
            residuals_sum += fit[1][0] if len(fit[1]) > 0 else 0.0
    elastic_tensor = ElasticTensor.from_voigt(c_ij)

    return {
        "elastic_tensor": elastic_tensor.zeroed(tolerance),
        "residuals_sum": residuals_sum,
    }