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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,89 @@ | ||
| import numpy as np | ||
| from pybispectrum import calc_bispectrum_names | ||
| from fitsnap3lib.scrapers.ase_funcs import get_apre, create_shared_arrays | ||
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| def ase_scraper( | ||
| s, frames, energies, forces, stresses=[[0, 0, 0], [0, 0, 0], [0, 0, 0]] | ||
| ): | ||
| """ | ||
| Custom function to allocate shared arrays used in Calculator and build the internal list of | ||
| dictionaries `data` of configuration info. Customized version of `fitsnap3lib.scrapers.ase_funcs`. | ||
| Args: | ||
| s: fitsnap instance. | ||
| frames: list or array of ASE atoms objects. | ||
| energies: array of energies. | ||
| forces: array of forces for all configurations. | ||
| stresses: array of stresses for all configurations. | ||
| Creates a list of data dictionaries `s.data` suitable for fitsnap descriptor calculation. | ||
| If running in parallel, this list will be distributed over procs, so that each proc will have a | ||
| portion of the list. | ||
| """ | ||
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| create_shared_arrays(s, frames) | ||
| s.data = [ | ||
| collate_data(a, e, f, s) | ||
| for (a, e, f, s) in zip(frames, energies, forces, stresses) | ||
| ] | ||
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| def collate_data(atoms, energy, forces, stresses): | ||
| """ | ||
| Function to organize fitting data for FitSNAP from ASE atoms objects. | ||
| Args: | ||
| atoms: ASE atoms object for a single configuration of atoms. | ||
| energy: energy of a configuration. | ||
| forces: numpy array of forces for a configuration. | ||
| stresses: numpy array of stresses for a configuration. | ||
| Returns a fitsnap data dictionary for a single configuration. | ||
| """ | ||
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| # make a data dictionary for this config | ||
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| apre = get_apre(cell=atoms.cell) | ||
| R = np.dot(np.linalg.inv(atoms.cell), apre) | ||
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| positions = np.matmul(atoms.get_positions(), R) | ||
| cell = apre.T | ||
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| data = {} | ||
| data["PositionsStyle"] = "angstrom" | ||
| data["AtomTypeStyle"] = "chemicalsymbol" | ||
| data["StressStyle"] = "bar" | ||
| data["LatticeStyle"] = "angstrom" | ||
| data["EnergyStyle"] = "electronvolt" | ||
| data["ForcesStyle"] = "electronvoltperangstrom" | ||
| data["Group"] = "Displaced_BCC" | ||
| data["File"] = None | ||
| data["Stress"] = stresses | ||
| data["Positions"] = positions | ||
| data["Energy"] = energy | ||
| data["AtomTypes"] = atoms.get_chemical_symbols() | ||
| data["NumAtoms"] = len(atoms) | ||
| data["Forces"] = forces | ||
| data["QMLattice"] = cell | ||
| data["test_bool"] = 0 | ||
| data["Lattice"] = cell | ||
| data["Rotation"] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) | ||
| data["Translation"] = np.zeros((len(atoms), 3)) | ||
| data["eweight"] = 1.0 | ||
| data["fweight"] = 1.0 / 150.0 | ||
| data["vweight"] = 0.0 | ||
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| return data | ||
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| def subsample_twojmax(total_bispect, twojmax_lst): | ||
| bi_spect_names_str_lst = [str(lst) for lst in total_bispect] | ||
| twojmax_master_str_lst = [ | ||
| [str(lst) for lst in calc_bispectrum_names(twojmax=tjm)] for tjm in twojmax_lst | ||
| ] | ||
| ind_lst = [ | ||
| [desc in desc_lst for desc in bi_spect_names_str_lst] | ||
| for desc_lst in twojmax_master_str_lst | ||
| ] | ||
| return ind_lst |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,101 @@ | ||
| from mpi4py import MPI | ||
| from fitsnap3lib.fitsnap import FitSnap | ||
| from pyiron_base import PythonTemplateJob, DataContainer | ||
| from pyiron_lanl.fitsnap.common import ase_scraper | ||
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| default_input = settings = { | ||
| "BISPECTRUM": { | ||
| "numTypes": 1, | ||
| "twojmax": 8, | ||
| "rcutfac": 4.812302818, | ||
| "rfac0": 0.99363, | ||
| "rmin0": 0.0, | ||
| "wj": 1.0, | ||
| "radelem": 0.5, | ||
| "type": "Be", | ||
| "wselfallflag": 0, | ||
| "chemflag": 0, | ||
| "bzeroflag": 0, | ||
| "quadraticflag": 0, | ||
| }, | ||
| "CALCULATOR": { | ||
| "calculator": "LAMMPSSNAP", | ||
| "energy": 1, # Calculate energy descriptors | ||
| "force": 1, # Calculate force descriptors | ||
| "stress": 0, # Calculate virial descriptors | ||
| }, | ||
| "REFERENCE": { | ||
| "units": "metal", | ||
| "atom_style": "atomic", | ||
| "pair_style": "hybrid/overlay zero 10.0 zbl 4.0 4.8", | ||
| "pair_coeff1": "* * zero", | ||
| "pair_coeff2": "1 1 zbl 74 74", | ||
| }, | ||
| "SOLVER": {"solver": "SVD", "compute_testerrs": 1, "detailed_errors": 1}, | ||
| "EXTRAS": { | ||
| "dump_descriptors": 0, | ||
| "dump_truth": 0, | ||
| "dump_weights": 0, | ||
| "dump_dataframe": 0, | ||
| }, | ||
| "MEMORY": {"override": 0}, | ||
| } | ||
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| class FitsnapJob(PythonTemplateJob): | ||
| def __init__(self, project, job_name): | ||
| super(FitsnapJob, self).__init__(project, job_name) | ||
| self.__version__ = "0.1" | ||
| self.__name__ = "FitsnapJob" | ||
| self.input.update(default_input) | ||
| self._lst_of_struct = [] | ||
| self._lst_of_energies = [] | ||
| self._lst_of_forces = [] | ||
| self._coefficients = [] | ||
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| @property | ||
| def list_of_structures(self): | ||
| return self._lst_of_struct | ||
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| @list_of_structures.setter | ||
| def list_of_structures(self, structure_lst): | ||
| self._lst_of_struct = structure_lst | ||
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| @property | ||
| def list_of_energies(self): | ||
| return self._lst_of_energies | ||
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| @list_of_energies.setter | ||
| def list_of_energies(self, energies): | ||
| self._lst_of_energies = energies | ||
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| @property | ||
| def coefficients(self): | ||
| return self._coefficients | ||
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| @property | ||
| def list_of_forces(self): | ||
| return self._lst_of_forces | ||
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| @list_of_forces.setter | ||
| def list_of_forces(self, forces): | ||
| self._lst_of_forces = forces | ||
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| def run_static(self): | ||
| comm = MPI.COMM_WORLD | ||
| input_dict = self.input.to_builtin() | ||
| snap = FitSnap(input_dict, comm=comm, arglist=["--overwrite"]) | ||
| ase_scraper( | ||
| snap, self._lst_of_struct, self._lst_of_energies, self._lst_of_forces | ||
| ) | ||
| snap.process_configs() | ||
| snap.solver.perform_fit() | ||
| self._coefficients = snap.solver.fit | ||
| self.status.finished = True | ||
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| def to_hdf(self, hdf=None, group_name=None): | ||
| super(FitsnapJob, self).to_hdf(hdf=hdf, group_name=group_name) | ||
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| def from_hdf(self, hdf=None, group_name=None): | ||
| super(FitsnapJob, self).from_hdf(hdf=hdf, group_name=group_name) |