Uteva, E., Graham, R., Wilkinson, R.D. orcid.org/0000-0001-7729-7023 et al. (1 more author) (2018) Active learning in Gaussian process interpolation of potential energy surfaces. Journal of Chemical Physics, 149 (17). 174114. ISSN 0021-9606
Abstract
Three active learning schemes are used to generate training data for Gaussian process interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the lowest predictive error using the fewest points and therefore act as an alternative to the status quo methods involving grid-based sampling or space-filling designs like Latin hypercubes (LHC). Results are presented for three molecular systems: CO2–Ne, CO2–H2, and Ar3. For each system, two of the active learning schemes proposed notably outperform LHC designs of comparable size, and in two of the systems, produce an error value an order of magnitude lower than the one produced by the LHC method. The procedures can be used to select a subset of points from a large pre-existing data set, to select points to generate data de novo, or to supplement an existing data set to improve accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Authors. This is an author produced version of a paper subsequently published in Journal of Chemical Physics. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Nov 2018 12:43 |
Last Modified: | 08 Nov 2018 12:51 |
Published Version: | https://doi.org/10.1063/1.5051772 |
Status: | Published |
Publisher: | AIP Publishing |
Refereed: | Yes |
Identification Number: | 10.1063/1.5051772 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138375 |