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Randomly-displaced methane configurations

Sergey Pozdnyakov1*, Michael Willatt1*, Michele Ceriotti1,2*

1 Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Corresponding authors emails: sergey.pozdnyakov@epfl.ch, m.willatt@fkf.mpg.de, michele.ceriotti@epfl.ch
DOI10.24435/materialscloud:qy-dp [version v2]

Publication date: Sep 18, 2020

How to cite this record

Sergey Pozdnyakov, Michael Willatt, Michele Ceriotti, Randomly-displaced methane configurations, Materials Cloud Archive 2020.110 (2020), doi: 10.24435/materialscloud:qy-dp.


Most of the datasets to benchmark machine-learning models contain minimum-energy structures, or small fluctuations around stable geometries, and focus on the diversity of chemical compositions, or the presence of different phases. This dataset provides a large number (7732488) configurations for a simple CH4 composition, that are generated in an almost completely unbiased fashion. Hydrogen atoms are randomly distributed in a 3A sphere centered around the carbon atom, and the only structures that are discarded are those with atoms that are closer than 0.5A, or such that the reference DFT calculation does not converge. This dataset is ideal to benchmark structural representations and regression algorithms, verifying whether they allow reaching arbitrary accuracy in the data rich regime.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.


File name Size Description
1.4 KiB readme
1.1 GiB 7732488 random methane molecules along with their dft energies and forces in the extended xyz format


Files and data are licensed under the terms of the following license: Creative Commons Attribution Non Commercial 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


dataset DFT Methane atomistic machine learning SNSF MARVEL

Version history:

2020.110 (version v2) [This version] Sep 18, 2020 DOI10.24435/materialscloud:qy-dp
2020.105 (version v1) Sep 04, 2020 DOI10.24435/materialscloud:s6-nq