Journal article · Preprint article
Machine learning with bond information for local structure optimizations in surface science
Department of Physics, Technical University of Denmark1
Computational Atomic-scale Materials Design, Department of Physics, Technical University of Denmark2
Stanford University3
Department of Energy Conversion and Storage, Technical University of Denmark4
Catalysis Theory Center, Department of Physics, Technical University of Denmark5
Atomic Scale Materials Modelling, Department of Energy Conversion and Storage, Technical University of Denmark6
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.
Language: | English |
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Publisher: | AIP Publishing LLC |
Year: | 2020 |
Pages: | 234116 |
ISSN: | 10897690 and 00219606 |
Types: | Journal article and Preprint article |
DOI: | 10.1063/5.0033778 |
ORCIDs: | 0000-0001-5434-6435 , Kaappa, Sami Juhani , 0000-0002-1727-0862 , Bligaard, Thomas and Jacobsen, Karsten Wedel |