GST-GAP-22

The complete dataset used for training the GST-GAP-22 interatomic potential, as labelled using the PBE functional. This dataset covers a range of compositions along the \(\text{GeTe} \rightarrow \text{Sb}_2\text{Te}_3\) pseudo-binary line, and was created using a two-step iterative process. More details are available in the paper’s supplementary information. The original data were obtained from Zenodo.

>>> from load_atoms import load_dataset
>>> load_dataset("GST-GAP-22")
GST-GAP-22:
    structures: 2,692
    atoms: 341,132
    species:
        Te: 54.51%
        Ge: 23.64%
        Sb: 21.85%
    properties:
        per atom: (forces)
        per structure: (config_type, energy, sub_config, 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{Zhou-23-10,
    title = {Device-Scale Atomistic Modelling of Phase-Change Memory Materials},
    author = {Zhou, Yuxing and Zhang, Wei and Ma, En and Deringer, Volker L.},
    year = {2023},
    journal = {Nature Electronics},
    volume = {6},
    number = {10},
    pages = {746--754},
    doi = {10.1038/s41928-023-01030-x},
}

Properties

Per-atom:

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

Per-structure:

Property	Units	Type	Description
energy	eV	float64	total structure energy (PBE DFT)
virial	eV	ndarray(3, 3)	virial stress tensor (PBE DFT)
config_type		str	category of structure

Miscellaneous information

GST-GAP-22 is imported as an InMemoryAtomsDataset:

Importer script for GST-GAP-22

from load_atoms.database.backend import BASE_GITHUB_URL, SingleFileImporter
from load_atoms.database.internet import FileDownload


class Importer(SingleFileImporter):
    @classmethod
    def file_to_download(cls) -> FileDownload:
        return FileDownload(
            url=f"{BASE_GITHUB_URL}/GST-GAP-22/refitted_GST-GAP-22_PBE.xyz",
            expected_hash="e4c467026dc0",
        )

DatabaseEntry for GST-GAP-22

name: GST-GAP-22
year: 2022
description: |
    The complete dataset used for training the `GST-GAP-22 <https://doi.org/10.1038/s41928-023-01030-x>`_ interatomic potential,
    as labelled using the PBE functional.
    This dataset covers a range of compositions along the :math:`\text{GeTe} \rightarrow \text{Sb}_2\text{Te}_3` pseudo-binary line, and
    was created using a two-step iterative process. More details are available in the paper's `supplementary information <https://static-content.springer.com/esm/art%3A10.1038%2Fs41928-023-01030-x/MediaObjects/41928_2023_1030_MOESM1_ESM.pdf>`__.
    The original data were obtained from `Zenodo <https://zenodo.org/records/8208202>`_.
category: Potential Fitting
minimum_load_atoms_version: 0.2
citation: |
    @article{Zhou-23-10,
        title = {Device-Scale Atomistic Modelling of Phase-Change Memory Materials},
        author = {Zhou, Yuxing and Zhang, Wei and Ma, En and Deringer, Volker L.},
        year = {2023},
        journal = {Nature Electronics},
        volume = {6},
        number = {10},
        pages = {746--754},
        doi = {10.1038/s41928-023-01030-x},
    }
license: CC BY 4.0
per_atom_properties:
    forces:
        desc: force vectors (PBE DFT)
        units: eV/Å
per_structure_properties:
    energy:
        desc: total structure energy (PBE DFT)
        units: eV
    virial:
        desc: virial stress tensor (PBE DFT)
        units: eV
    config_type:
        desc: category of structure
representative_structure: 1894


# TODO: remove after Dec 2024
# backwards compatability: unused as of 0.3.0
files:
     - name: refitted_GST-GAP-22_PBE.xyz
       hash: e4c467026dc0