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Applications and Advances in Machine Learning Force Fields

2023; American Chemical Society; Volume: 63; Issue: 22 Linguagem: Inglês

10.1021/acs.jcim.3c00889

1549-960X

Shiru Wu, Xiaowei Yang, Xun Zhao, Zhipu Li, Min Lü, Xiaoji Xie, Jiaxu Yan,

Protein Structure and Dynamics

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.