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Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials
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van der Oord, Cas, Dusson, Geneviève, Csanyi, Gabor and Ortner, Christoph (2020) Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials. Machine Learning : Science and Technology, 1 (1). 015004. doi:10.1088/2632-2153/ab527c ISSN 2632-2153.
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Official URL: https://doi.org/10.1088/2632-2153/ab527c
Abstract
We investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The 'atomic body-ordered permutation-invariant polynomials' comprise a systematic basis and are constructed to preserve the symmetry of the potential energy function with respect to rotations and permutations. In contrast to kernel based and artificial neural network models, the explicit decomposition of the total energy as a sum of atomic body-ordered terms allows to keep the dimensionality of the fit reasonably low, up to just 10 for the 5-body terms. The explainability of the potential is aided by this decomposition, as the low body-order components can be studied and interpreted independently. Moreover, although polynomial basis functions are thought to extrapolate poorly, we show that the low dimensionality combined with careful regularisation actually leads to better transferability than the high dimensional, kernel based Gaussian Approximation Potential.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QC Physics Q Science > QD Chemistry |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Polynomials , Density functionals, Born-Oppenheimer approximation , Molecular dynamics -- Mathematical models, Quantum chemistry | |||||||||||||||
Journal or Publication Title: | Machine Learning : Science and Technology | |||||||||||||||
Publisher: | IOP Publishing Ltd | |||||||||||||||
ISSN: | 2632-2153 | |||||||||||||||
Official Date: | 4 February 2020 | |||||||||||||||
Dates: |
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Volume: | 1 | |||||||||||||||
Number: | 1 | |||||||||||||||
Article Number: | 015004 | |||||||||||||||
DOI: | 10.1088/2632-2153/ab527c | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Re-use Statement: | “This is an author-created, un-copyedited version of an article accepted for publication/published in Machine Learning : Science and Technology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2632-2153/ab527c | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Copyright Holders: | © 2020 The Author(s). Published by IOP Publishing Ltd | |||||||||||||||
Date of first compliant deposit: | 4 November 2019 | |||||||||||||||
Date of first compliant Open Access: | 17 February 2021 | |||||||||||||||
RIOXX Funder/Project Grant: |
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