Deep Potential fitting and mechanical properties study of MgAlSi alloy

doi:10.1016/j.commatsci.2024.112966

IR > 计算机与通信学院

	Deep Potential fitting and mechanical properties study of MgAlSi alloy
	Zhu, Chang-sheng1,2 ; Dong, Wen-jing 1; Gao, Zi-hao 1; Wang, Li-jun 1; Li, Guang-zhao 1
	2024-04-25
发表期刊	Computational Materials Science
ISSN	0927-0256
卷号	239
摘要	MgAlSi alloy materials have the main properties of light weight and high strength, good electrical and thermal conductivity and corrosion resistance, and have various applications in the industrial field, making an important contribution to the realization of lightweight and high performance needs. In order to be able to predict the material properties of MgAlSi alloys with a high degree of accuracy, this paper develops for the first time an interatomic potential function for MgAlSi alloys based on a neural network machine learning approach. The effectiveness of the developed machine learning potentials is verified by analyzing the problems encountered during the training process and the errors of the finally obtained potential functions, and comparing some of the radial distribution functions, coordination numbers, and predictions of properties such as the equation of state, lattice constants, shear modulus and bulk modulus with those of AIMD. It is found that the performance error of the deep potential model is basically kept in the same order of magnitude as that of DFT calculations, the computational speed can be up to nearly a thousand times that of DFT, and the computational cost is linearly related to the atomic number, which is well suited for large-scale molecular dynamics simulations, and it will provide a promising solution for accurate large-scale molecular dynamics simulations. © 2024 Elsevier B.V.
关键词	Atoms Computational chemistry Corrosion resistance Corrosion resistant alloys Distribution functions Elastic moduli High strength alloys Lattice constants Machine learning Magnesium alloys Molecular dynamics Potential energy Quantum chemistry Silicon alloys Alloy materials Deep potential Dynamics simulation High-strength Industrial fields Large-scale molecular dynamics Light weight Material simulation Potential-energy surfaces Property
DOI	10.1016/j.commatsci.2024.112966
收录类别	EI
语种	英语
出版者	Elsevier B.V.
EI入藏号	20241315815590
EI主题词	Aluminum alloys
EI分类号	531 Metallurgy and Metallography ; 531.1 Metallurgy ; 539.1 Metals Corrosion ; 541.2 Aluminum Alloys ; 542.2 Magnesium and Alloys ; 549.2 Alkaline Earth Metals ; 549.3 Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth Metals ; 723.4 Artificial Intelligence ; 801 Chemistry ; 801.4 Physical Chemistry ; 921.6 Numerical Methods ; 922.1 Probability Theory ; 931.3 Atomic and Molecular Physics ; 933.1.1 Crystal Lattice ; 951 Materials Science
原始文献类型	Journal article (JA)
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170279
专题	计算机与通信学院
通讯作者	Zhu, Chang-sheng
作者单位	1.College of Computer and Communication, Lanzhou University of Technology, Lanzhou; 730050, China; 2.State Key Laboratory of Gansu Advanced Processing and Recycling of Non-Ferrous Metal, Lanzhou University of Technology, Lanzhou; 730050, China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhu, Chang-sheng,Dong, Wen-jing,Gao, Zi-hao,et al. Deep Potential fitting and mechanical properties study of MgAlSi alloy[J]. Computational Materials Science,2024,239.
APA	Zhu, Chang-sheng,Dong, Wen-jing,Gao, Zi-hao,Wang, Li-jun,&Li, Guang-zhao.(2024).Deep Potential fitting and mechanical properties study of MgAlSi alloy.Computational Materials Science,239.
MLA	Zhu, Chang-sheng,et al."Deep Potential fitting and mechanical properties study of MgAlSi alloy".Computational Materials Science 239(2024).