Machine learning with bond information for local structure optimizations in surface science

Estefania Garijo del Rio, Sami Juhani Kaappa, José A. Garrido Torres, Thomas Bligaard, Karsten Wedel Jacobsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.
Original languageEnglish
Article number234116
JournalJournal of Chemical Physics
Volume153
Issue number23
ISSN0021-9606
DOIs
Publication statusPublished - 2020

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