CIOMP OpenIR
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
ISSN15499596
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.
DOI10.1021/acs.jcim.3c00889
URL查看原文
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Applications and Adv(9073KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan]的文章
百度学术
百度学术中相似的文章
[S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan]的文章
必应学术
必应学术中相似的文章
[S. Wu, X. Yang, X. Zhao, Z. Li, M. Lu, X. Xie and J. Yan]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Applications and Advances in Machine Learning Force Fields.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。