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Gaussian Process Models of Potential Energy Surfaces with Boundary Optimisation

Broad, Jack; Preston, Simon; Wheatley, Richard J; Graham, Richard S

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Authors

Jack Broad

SIMON PRESTON simon.preston@nottingham.ac.uk
Professor of Statistics and Applied Mathematics

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RICHARD WHEATLEY RICHARD.WHEATLEY@NOTTINGHAM.AC.UK
Associate Professor & Reader in Theoretical Chemistry

RICHARD GRAHAM richard.graham@nottingham.ac.uk
Professor of Applied Mathematics



Abstract

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at long range and the cross-over distance between this model and the Gaussian process is learnt from the training data. Results are presented for different implementations of this procedure, known as boundary optimisation, across the following dimer systems: CONe , HF-Ne, HF-Na + , CO 2-Ne and (CO 2) 2. The technique reduces the number of training points, at fixed accuracy, by up to ∼ 49 %, compared to our previous work based on a sequential learning technique. The approach is readily transferable to other statistical methods of prediction or modelling problems.

Journal Article Type Article
Acceptance Date Sep 29, 2021
Online Publication Date Oct 13, 2021
Publication Date Oct 13, 2021
Deposit Date Oct 7, 2021
Publicly Available Date Oct 13, 2021
Journal Journal of Chemical Physics
Print ISSN 0021-9606
Peer Reviewed Peer Reviewed
Volume 155
Issue 14
Article Number 144106
DOI https://doi.org/10.1063/5.0063534
Public URL https://nottingham-repository.worktribe.com/output/6395789
Publisher URL https://aip.scitation.org/doi/10.1063/5.0063534
Additional Information This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Broad, J., Preston, S., Wheatley, R. J., & Graham, R. S. (2021). Gaussian Process Models of Potential Energy Surfaces with Boundary Optimisation. Journal of Chemical Physics, 155(14) and may be found at https://aip.scitation.org/doi/10.1063/5.0063534.

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