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Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials

文献类型：期刊论文

作者Yuan, Xiaoze3,4,5; Zhou, Yuwei2,4,5; Peng, Qing1,3; Yang, Yong4,5; Li, Yongwang2,4,5; Wen, Xiaodong2,4,5
刊名NPJ COMPUTATIONAL MATERIALS
出版日期2023-01-20
卷号9期号:1页码:9
DOI10.1038/s41524-023-00967-z
通讯作者Zhou, Yuwei(zhouyuwei_kt@163.com) ; Peng, Qing(pengqing@imech.ac.cn) ; Wen, Xiaodong(wxd@sxicc.ac.cn)
英文摘要Chemical-disordered materials have a wide range of applications whereas the determination of their structures or configurations is one of the most important and challenging problems. Traditional methods are extremely inefficient or intractable for large systems due to the notorious exponential-wall issue that the number of possible structures increase exponentially for N-body systems. Herein, we introduce an efficient approach to predict the thermodynamically stable structures of chemical-disordered materials via active-learning accompanied by first-principles calculations. Our method, named LAsou, can efficiently compress the sampling space and dramatically reduce the computational cost. Three distinct and typical finite-size systems are investigated, including the anion-disordered BaSc(OxF1-x)(3) (x = 0.667), the cation-disordered Ca1-xMnxCO3 (x = 0.25) with larger size and the defect-disordered epsilon-FeCx (x = 0.5) with larger space. The commonly used enumeration method requires to explicitly calculate 2664, 1033, and 10496 configurations, respectively, while the LAsou method just needs to explicitly calculate about 15, 20, and 10 configurations, respectively. Besides the finite-size system, our LAsou method is ready for quasi-infinite size systems empowering materials design.
WOS关键词ALLOYS ; ENERGY ; OPTIMIZATION ; PEROVSKITE ; REGRESSION ; ALGORITHM
资助项目National Key R&D Program of China[2022YFA1604103] ; National Science Fund for Distinguished Young Scholars of China[22225206] ; National Natural Science Foundation of China[21972157] ; National Natural Science Foundation of China[21972160] ; National Natural Science Foundation of China[21703272] ; CAS Project for Young Scientists in Basic Research[YSBR-005] ; Key Research Program of Frontier Sciences CAS[ZDBS-LY-7007] ; Major Research plan of the National Natural Science Foundation of China[92045303] ; CAS Project for Internet Security and Information Technology[CAS-WX2021SF0110] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region of China[2021GG0309] ; Beijing Advanced Innovation Center for Materials Genome Engineering, Synfuels China, Co. Ltd ; Institute of Coal Chemistry (CAS) ; LiYing Program of the Institute of Mechanics, Chinese Academy of Sciences[E1Z1011001]
WOS研究方向Chemistry ; Materials Science
语种英语
WOS记录号WOS:000918450900001
资助机构National Key R&D Program of China ; National Science Fund for Distinguished Young Scholars of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; Key Research Program of Frontier Sciences CAS ; Major Research plan of the National Natural Science Foundation of China ; CAS Project for Internet Security and Information Technology ; Science and Technology Plan Project of Inner Mongolia Autonomous Region of China ; Beijing Advanced Innovation Center for Materials Genome Engineering, Synfuels China, Co. Ltd ; Institute of Coal Chemistry (CAS) ; LiYing Program of the Institute of Mechanics, Chinese Academy of Sciences
源URL[http://dspace.imech.ac.cn/handle/311007/91691]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Zhou, Yuwei; Peng, Qing; Wen, Xiaodong
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Ind Univ Cooperat Base Beijing Informat S&T Univ &, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Mech, State Key Lab Nolinear Mech, Beijing 100190, Peoples R China
4.Synfuels China Technol Co Ltd, Natl Energy Ctr Coal Clean Fuels, Beijing 101400, Peoples R China
5.Chinese Acad Sci, Inst Coal Chem, State Key Lab Coal Convers, Taiyuan 030001, Peoples R China
推荐引用方式
GB/T 7714		 Yuan, Xiaoze,Zhou, Yuwei,Peng, Qing,et al. Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials[J]. NPJ COMPUTATIONAL MATERIALS,2023,9(1):9.
APA Yuan, Xiaoze,Zhou, Yuwei,Peng, Qing,Yang, Yong,Li, Yongwang,&Wen, Xiaodong.(2023).Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials.NPJ COMPUTATIONAL MATERIALS,9(1),9.
MLA Yuan, Xiaoze,et al."Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials".NPJ COMPUTATIONAL MATERIALS 9.1(2023):9.

入库方式： OAI收割

来源：力学研究所

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