Zhang, Yunyu, Butler, Keith T., Higham, Michael D. and Catlow, C. Richard A. ![]() ![]() |
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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 |
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Date Type: | Published Online |
Status: | Published |
Schools: | 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: | 17 Mar 2025 10:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/176908 |
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