Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion

doi:10.1016/j.cplett.2023.140646

IMR OpenIR

	Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion
	Li, Yongle 1; Xu, Feng 1; Hou, Long 1; Sun, Luchao 2; Su, Haijun 3; Li, Xi 1,4; Ren, Wei 1
通讯作者	Li, Yongle(yongleli@shu.edu.cn) ; Ren, Wei(renwei@shu.edu.cn)
	2023-09-01
发表期刊	CHEMICAL PHYSICS LETTERS
ISSN	0009-2614
卷号	826 页码:6
摘要	The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.
关键词	Molecular dynamics Atomic cluster expansion Fe-Co Alloy Density functional theory Phase transition Force field
资助者	National Natural Science Foundation of China ; Science and Technology Commission of Shanghai Municipality ; Key Research Project of Zhejiang Laboratory ; High Performance Computing Center, Shanghai University
DOI	10.1016/j.cplett.2023.140646
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[52130204] ; National Natural Science Foundation of China[12074241] ; National Natural Science Foundation of China[11929401] ; Science and Technology Commission of Shanghai Municipality[22XD1400900] ; Science and Technology Commission of Shanghai Municipality[20501130600] ; Science and Technology Commission of Shanghai Municipality[20QA1401000] ; Science and Technology Commission of Shanghai Municipality[21JC1402600] ; Science and Technology Commission of Shanghai Municipality[21JC1402700] ; Key Research Project of Zhejiang Laboratory[2021PE0AC02] ; High Performance Computing Center, Shanghai University
WOS研究方向	Chemistry ; Physics
WOS类目	Chemistry, Physical ; Physics, Atomic, Molecular & Chemical
WOS记录号	WOS:001027725000001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/178532
专题	中国科学院金属研究所
通讯作者	Li, Yongle; Ren, Wei
作者单位	1.Shanghai Univ, Mat Genome Inst, Int Ctr Quantum & Mol Struct, Dept Phys,State Key Lab Adv Special Steels, Shanghai 200444, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Peoples R China 4.Shanghai Jiao Tong Univ, Shanghai Key Lab Adv High Temp Mat & Precis Formin, Shanghai 200240, Peoples R China
推荐引用方式 GB/T 7714	Li, Yongle,Xu, Feng,Hou, Long,et al. Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion[J]. CHEMICAL PHYSICS LETTERS,2023,826:6.
APA	Li, Yongle.,Xu, Feng.,Hou, Long.,Sun, Luchao.,Su, Haijun.,...&Ren, Wei.(2023).Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion.CHEMICAL PHYSICS LETTERS,826,6.
MLA	Li, Yongle,et al."Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion".CHEMICAL PHYSICS LETTERS 826(2023):6.