Applications and Advances in Machine Learning Force Fields

doi:10.1021/acs.jcim.3c00889

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	Applications and Advances in Machine Learning Force Fields
	S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan
	2023
发表期刊	Journal of Chemical Information and Modeling
ISSN	15499596
卷号	63 期号:22 页码:6972-6985
摘要	Force fields (FFs) form the basis of molecular simulations and have significant implications in diverse fields such as materials science, chemistry, physics, and biology. A suitable FF is required to accurately describe system properties. However, an off-the-shelf FF may not be suitable for certain specialized systems, and researchers often need to tailor the FF that fits specific requirements. Before applying machine learning (ML) techniques to construct FFs, the mainstream FFs were primarily based on first-principles force fields (FPFF) and empirical FFs. However, the drawbacks of FPFF and empirical FFs are high cost and low accuracy, respectively, so there is a growing interest in using ML as an effective and precise tool for reconciling this trade-off in developing FFs. In this review, we introduce the fundamental principles of ML and FFs in the context of machine learning force fields (MLFF). We also discuss the advantages and applications of MLFF compared to traditional FFs, as well as the MLFF toolkits widely employed in numerous applications. © 2023 American Chemical Society.
DOI	10.1021/acs.jcim.3c00889
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68032
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan. Applications and Advances in Machine Learning Force Fields[J]. Journal of Chemical Information and Modeling,2023,63(22):6972-6985.
APA	S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan.(2023).Applications and Advances in Machine Learning Force Fields.Journal of Chemical Information and Modeling,63(22),6972-6985.
MLA	S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan."Applications and Advances in Machine Learning Force Fields".Journal of Chemical Information and Modeling 63.22(2023):6972-6985.

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