REMatch plus SOS: Machine-learning-accelerated structure prediction for supported metal nanoclusters

Zhang, Yunyu, Butler, Keith T., Higham, Michael D. and Catlow, C. Richard A. ORCID: https://orcid.org/0000-0002-1341-1541 2025. REMatch plus SOS: Machine-learning-accelerated structure prediction for supported metal nanoclusters. Physical Review Materials 9 (3) , 033801. 10.1103/physrevmaterials.9.033801

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Abstract

Predicting stable structures of nanoclusters is crucial yet computationally demanding. This study presents a machine learning-based methodology designed to accelerate the prediction of stable structures in nanoclusters. By integrating local environment descriptors, with dimensionality reduction, kernel-based similarity measure, and outlier detection, we efficiently screen and select promising configurations, thus accelerating identification of global and local minimum structures. The approach is validated through rigorous optimization, demonstrating its capability to identify low-energy structures while significantly reducing computational costs. This method offers a robust framework for structural screening. Published by the American Physical Society 2025

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Chemistry
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2025-03-03
Publisher:	American Physical Society
Date of First Compliant Deposit:	17 March 2025
Date of Acceptance:	28 January 2025
Last Modified:	16 Jun 2025 15:17
URI:	https://orca.cardiff.ac.uk/id/eprint/176908

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