.. This file is autogenerated by dev/scripts/generate_page.py QM7 === .. grid:: 1 1 2 2 .. grid-item:: .. raw:: html :file: ../_static/visualisations/QM7.html .. grid-item:: :class: info-card 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. .. code-block:: pycon >>> 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: .. code-block:: latex @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**: .. list-table:: :header-rows: 1 * - Property - Units - Type - Description * - :code:`energy` - eV - :class:`~float64` - atomisation energy (DFT) Miscellaneous information ------------------------- ``QM7`` is imported as an :class:`~load_atoms.atoms_dataset.InMemoryAtomsDataset`: .. dropdown:: Importer script for :code:`QM7` .. literalinclude:: ../../../src/load_atoms/database/importers/qm7.py :language: python .. dropdown:: :class:`~load_atoms.database.DatabaseEntry` for :code:`QM7` .. code-block:: yaml 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 `_. 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