SiO2-GAP-22

The training database used to fit the GAP-22 potential for silica in: A Machine-Learned Interatomic Potential for Silica and Its Relation to Empirical Models. The dataset was generated using an iterative approach, in some cases driven by an emprical potential. More details are available in the supplementary information.

>>> from load_atoms import load_dataset
>>> load_dataset("SiO2-GAP-22")
SiO2-GAP-22:
    structures: 3,074
    atoms: 268,118
    species:
        O: 66.47%
        Si: 33.53%
    properties:
        per atom: (forces)
        per structure: (config_type, energy, free_energy, virial)

License

This dataset is licensed under the CC BY 4.0 license.

Citation

If you use this dataset in your work, please cite the following:

@article{Erhard-22-04,
  title = {
    A Machine-Learned Interatomic Potential for Silica
    and Its Relation to Empirical Models
  },
  author = {
    Erhard, Linus C. and Rohrer, Jochen
    and Albe, Karsten and Deringer, Volker L.
  },
  year = {2022},
  journal = {npj Computational Materials},
  volume = {8},
  number = {1},
  pages = {1--12},
}

Properties

Per-atom:

Property	Units	Type	Description
forces	eV/Å	ndarray(N, 3)	force vectors (DFT)

Per-structure:

Property	Units	Type	Description
energy	eV	float64	total structure energy (DFT)
virial	eV	ndarray(9,)	virial stress tensor (DFT)
stress	eV Å\({}^{-3}\)	ndarray(3, 3)	stress tensor (DFT) (- virial / cell.volume)
config_type		str	category of structure

Miscellaneous information

SiO2-GAP-22 is imported as an InMemoryAtomsDataset:

Importer script for SiO2-GAP-22

from __future__ import annotations

from pathlib import Path
from typing import Iterator

import ase.io
from ase import Atoms
from load_atoms.database.backend import BaseImporter, rename, unzip_file
from load_atoms.database.internet import FileDownload
from load_atoms.progress import Progress


class Importer(BaseImporter):
    @classmethod
    def files_to_download(cls) -> list[FileDownload]:
        return [
            FileDownload(
                url="https://zenodo.org/records/6353684/files/sio2_potential_data.zip",
                expected_hash="98ea6e58f6d9",
            )
        ]

    @classmethod
    def get_structures(
        cls, tmp_dir: Path, progress: Progress
    ) -> Iterator[Atoms]:
        contents = unzip_file(tmp_dir / "sio2_potential_data.zip", progress)

        for structure in ase.io.iread(
            contents / "sio2_potential_data/database/dataset.scan.2.xyz"
        ):
            yield rename(structure, {"virials": "virial"})

DatabaseEntry for SiO2-GAP-22

name: SiO2-GAP-22
year: 2022
description: |
    The training database used to fit the `GAP-22 potential for silica <https://zenodo.org/records/6353684>`_ in:
    `A Machine-Learned Interatomic Potential for Silica and Its Relation to Empirical Models <https://doi.org/10.1038/s41524-022-00768-w>`_.
    The dataset was generated using an iterative approach, in some cases driven by an emprical potential. More details are available in the
    `supplementary information <https://static-content.springer.com/esm/art%3A10.1038%2Fs41524-022-00768-w/MediaObjects/41524_2022_768_MOESM1_ESM.pdf>`_.
category: Potential Fitting
minimum_load_atoms_version: 0.2
license: CC BY 4.0
citation: |
    @article{Erhard-22-04,
      title = {
        A Machine-Learned Interatomic Potential for Silica
        and Its Relation to Empirical Models
      },
      author = {
        Erhard, Linus C. and Rohrer, Jochen
        and Albe, Karsten and Deringer, Volker L.
      },
      year = {2022},
      journal = {npj Computational Materials},
      volume = {8},
      number = {1},
      pages = {1--12},
    }
per_atom_properties:
    forces:
        desc: force vectors (DFT)
        units: eV/Å
per_structure_properties:
    energy:
        desc: total structure energy (DFT)
        units: eV
    virial:
        desc: virial stress tensor (DFT)
        units: eV
    stress:
        desc: |
            | stress tensor (DFT)
            | (:code:`- virial / cell.volume`)
        units: eV Å\ :math:`{}^{-3}`
    config_type:
        desc: category of structure


# TODO: remove after Dec 2024
# backwards compatability: unused as of 0.3.0
files:
    - url: https://zenodo.org/records/6353684/files/sio2_potential_data.zip
      hash: 98ea6e58f6d9
processing:
    - UnZip
    - SelectFile:
        file: sio2_potential_data/database/dataset.scan.2.xyz
    - ReadASE
    - Rename:
        virials: virial