.. 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