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Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

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Onat, Berk, Ortner, Christoph and Kermode, James R. (2020) Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials. The Journal of Chemical Physics, 153 (14). 144106. doi:10.1063/5.0016005

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Official URL: http://dx.doi.org/10.1063/5.0016005

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Abstract

Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QC Physics
Divisions: Faculty of Science > Engineering
Faculty of Science > Mathematics
Library of Congress Subject Headings (LCSH): Machine learning, Nuclear forces (Physics), Atomic structure, Neural networks (Computer science)
Journal or Publication Title: The Journal of Chemical Physics
Publisher: American Institute of Physics
ISSN: 0021-9606
Official Date: 12 October 2020
Dates:
DateEvent
12 October 2020Published
17 September 2020Accepted
Volume: 153
Number: 14
Article Number: 144106
DOI: 10.1063/5.0016005
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
RPG-2017-191Leverhulme Trusthttp://dx.doi.org/10.13039/501100000275
676580Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
EP/P022065/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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