Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential

doi:10.1016/j.actamat.2023.119041

IMECH-IR > 非线性力学国家重点实验室

	Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential
	Santosflorez, Pedro A ; Dai SC(戴仕诚); Yao, Yi ; Yanxon, Howard ; Li, Lin ; Wang YJ(王云江); Zhu, Qiang ; Yu, Xiaoxiang
发表期刊	ACTA MATERIALIA
	2023-08-15
卷号	255 页码:119041
ISSN	1359-6454
摘要	We utilize a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the chemical short-range order (SRO) influences on the BCC MoNbTaW alloy strengthening mechanism. The NN interatomic potential offers a transferable force field that exhibits accuracy comparable to density functional theory. This innovative NN potential is employed to examine the SRO effects on various aspects such as elasticity, vibrational modes, plasticity, and strength in the MoNbTaW multi-principal element alloy (MPEA). The findings reveal a significant attraction between Mo-Ta pairs, resulting in the formation of locally ordered B2 clusters. These clusters can be adjustedvia temperature and enhanced by Nb content. The presence of SRO leads to an increase in high-frequency phonon modes and introduces additional lattice friction to dislocation motion. This approach facilitates efficient compositional screening, paving the way for computational-guided materials design of novel MPEAs with enhanced performance. Furthermore, it opens up avenues for tuning the mechanical properties through optimization of the processing parameters.
关键词	Multi -principal element alloys Short-range order Machine -learning potential
DOI	10.1016/j.actamat.2023.119041
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:001018577700001
WOS研究方向	Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
项目资助者	NSFC [12072344] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; [TG-DMR180040]
论文分区	一类
力学所作者排名	1
RpAuthor	Zhu, Q (corresponding author), Univ Nevada, Dept Phys & Astron, Las Vegas, NV 89154 USA. ; Wang, YJ (corresponding author), Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China. ; Wang, YJ (corresponding author), Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China. ; Li, L (corresponding author), Univ Alabama, Dept Met & Mat Engn, Tuscaloosa, AL 35401 USA. ; Li, L (corresponding author), Arizona State Univ, Sch Engn Matter Transport & Energy, Tempe, AZ 85287 USA. ; Yu, XX (corresponding author), Northwestern Univ, Dept Mat Sci & Engn, Evanston, IL 60208 USA. ; Yu, XX (corresponding author), Novelis Global Res & Technol Ctr, Kennesaw, GA 30144 USA.
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/92430
专题	非线性力学国家重点实验室
作者单位	1.{Santos-Florez Pedro A., Yanxon Howard, Zhu Qiang} Univ Nevada Dept Phys & Astron Las Vegas NV 89154 USA 2.{Dai Shi-Cheng, Wang Yun-Jiang} Chinese Acad Sci Inst Mech State Key Lab Nonlinear Mech Beijing 100190 Peoples R China 3.{Dai Shi-Cheng, Wang Yun-Jiang} Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China 4.{Yao Yi, Li Lin} Univ Alabama Dept Met & Mat Engn Tuscaloosa AL 35401 USA 5.{Yao Yi, Li Lin} Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA 6.{Yu Xiao-Xiang} Northwestern Univ Dept Mat Sci & Engn Evanston IL 60208 USA 7.{Yu Xiao-Xiang} Novelis Global Res & Technol Ctr Kennesaw GA 30144 USA
推荐引用方式 GB/T 7714	Santosflorez, Pedro A,Dai SC,Yao, Yi,et al. Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential[J]. ACTA MATERIALIA,2023,255:119041.
APA	Santosflorez, Pedro A.,戴仕诚.,Yao, Yi.,Yanxon, Howard.,Li, Lin.,...&Yu, Xiaoxiang.(2023).Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential.ACTA MATERIALIA,255,119041.
MLA	Santosflorez, Pedro A,et al."Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential".ACTA MATERIALIA 255(2023):119041.

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