QM7

A collection of 7,165 saturated, small molecules containing up to 7 heavy atoms, with geometries relaxed using an empirical potential. Atomisation energies were calculated similarly to a FHI-AIMS implementation of the Perdew-Burke-Ernzerhof hybrid functional (PBE0). Original files were obtained from quantum-machine.org. Energies have been converted from kcal/mol to eV.

>>> from load_atoms import load_dataset
>>> load_dataset("QM7")
QM7:
    structures: 7,165
    atoms: 110,650
    species:
        H: 56.00%
        C: 32.32%
        N: 6.01%
        O: 5.40%
        S: 0.27%
    properties:
        per atom: ()
        per structure: (energy)

Citation

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

@inproceedings{Montavon-12,
    author = {
        Montavon, Gr\'{e}goire and Hansen, Katja
        and Fazli, Siamac and Rupp, Matthias
        and Biegler, Franziska and Ziehe, Andreas
        and Tkatchenko, Alexandre and Lilienfeld, Anatole
        and M\"{u}ller, Klaus-Robert
    },
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
    title = {
        Learning Invariant Representations of Molecules
        for Atomization Energy Prediction
    },
    volume = {25},
    year = {2012}
}
@article{Rupp-12,
    title = {
        Fast and Accurate Modeling of Molecular
        Atomization Energies with Machine Learning
    },
    author = {
        Rupp, Matthias and Tkatchenko, Alexandre
        and M{\"u}ller, Klaus-Robert and {von Lilienfeld}, O. Anatole
    },
    year = {2012},
    journal = {Physical Review Letters},
    volume = {108},
    number = {5},
    pages = {058301},
    doi = {10.1103/PhysRevLett.108.058301}
}

Properties

Per-structure:

Property	Units	Type	Description
energy	eV	float64	atomisation energy (DFT)

Miscellaneous information

QM7 is imported as an InMemoryAtomsDataset:

Importer script for QM7

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}/QM7/QM7.extxyz",
            expected_hash="c9dcec505f4d",
        )

DatabaseEntry for QM7

name: QM7
year: 2012
description: |
    A collection of 7,165 saturated, small molecules containing up to 7 heavy atoms,
    with geometries relaxed using an empirical potential. Atomisation energies were calculated similarly to a FHI-AIMS implementation of the Perdew-Burke-Ernzerhof hybrid functional (PBE0).
    Original files were obtained from `quantum-machine.org <http://quantum-machine.org/datasets/>`_.
    Energies have been converted from kcal/mol to eV.
category: Benchmarks
minimum_load_atoms_version: 0.2
per_structure_properties:
    energy:
        desc: atomisation energy (DFT)
        units: eV
representative_structure: 6492
citation: |
    @inproceedings{Montavon-12,
        author = {
            Montavon, Gr\'{e}goire and Hansen, Katja
            and Fazli, Siamac and Rupp, Matthias
            and Biegler, Franziska and Ziehe, Andreas
            and Tkatchenko, Alexandre and Lilienfeld, Anatole
            and M\"{u}ller, Klaus-Robert
        },
        booktitle = {Advances in Neural Information Processing Systems},
        editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
        title = {
            Learning Invariant Representations of Molecules
            for Atomization Energy Prediction
        },
        volume = {25},
        year = {2012}
    }
    @article{Rupp-12,
        title = {
            Fast and Accurate Modeling of Molecular
            Atomization Energies with Machine Learning
        },
        author = {
            Rupp, Matthias and Tkatchenko, Alexandre
            and M{\"u}ller, Klaus-Robert and {von Lilienfeld}, O. Anatole
        },
        year = {2012},
        journal = {Physical Review Letters},
        volume = {108},
        number = {5},
        pages = {058301},
        doi = {10.1103/PhysRevLett.108.058301}
    }


# TODO: remove after Dec 2024
# backwards compatability: unused as of 0.3.0
files:
     - name: QM7.extxyz
       hash: c9dcec505f4d