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Molecular dynamics and machine learning in catalysts

Liu, Wenxiang, Zhu, Yang, Wu, Yongqiang, Chen, Cen, Hong, Yang, Yue, Yanan, Zhang, Jingchao and Hou, Bo ORCID: 2021. Molecular dynamics and machine learning in catalysts. Catalysts 11 (9) , 1129. 10.3390/catal11091129

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Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Additional Information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).
Publisher: MDPI
ISSN: 2073-4344
Date of First Compliant Deposit: 20 November 2021
Date of Acceptance: 18 September 2021
Last Modified: 24 May 2023 00:43

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