气候变化领域集成服务门户(CCPortal): Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors

CCPortal

DOI	10.1016/j.scib.2021.04.029
	Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors
	Zhao Q.; Avdeev M.; Chen L.; Shi S.
发表日期	2021
ISSN	20959273
起始页码	1401
结束页码	1408
卷号	66 期号:14
英文摘要	Rational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (Ea) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li+ conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for Ea in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an Ea prediction model is developed to the coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li+ conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed Ea prediction model directs us to optimize and design new Li-argyrodites with lower Ea, such as Li6–xPS5–xCl1+x (<0.322 eV), Li6+xPS5+xBr1–x (<0.273 eV), Li6+xPS5+xBr0.25I0.75–x (<0.352 eV), Li6+(5–n)yP1–yNyS5I (<0.420 eV), Li6+(5–n)yAs1–yNyS5I (<0.371 eV), Li6+(5–n)yAs1–yNySe5I (<0.450 eV), by broadening bottleneck size, invoking site disorder and activating concerted Li+ conduction. This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials. © 2021 Science China Press
关键词	Cubic Li-argyrodites Hierarchically encoding crystal structure-based (HECS) descriptors Machine learning Predicting activation energy Solid-state electrolytes (SSEs)
英文关键词	Activation energy; Crystal structure; Encoding (symbols); Forecasting; Ionic conduction in solids; Machine learning; Mean square error; Signal encoding; Solid state devices; Solid-State Batteries; X ray photoelectron spectroscopy; Crystals structures; Cubic li-argyrodite; Descriptors; Encodings; Hierarchically encoding crystal structure-based descriptor; Li$++$; Machine-learning; Predicting activation energy; Solid-state electrolyte; Solid electrolytes
语种	英语
来源期刊	Science Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/207684
作者单位	Materials Genome Institute, Shanghai University, Shanghai, 200444, China; Australian Nuclear Science and Technology Organization, New Illawarra Rd, Lucas HeightsNSW 2234, Australia; School of Chemistry, The University of Sydney, Sydney, 2006, Australia; Key Laboratory for Renewable Energy, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China; School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
推荐引用方式 GB/T 7714	Zhao Q.,Avdeev M.,Chen L.,et al. Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors[J],2021,66(14).
APA	Zhao Q.,Avdeev M.,Chen L.,&Shi S..(2021).Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors.Science Bulletin,66(14).
MLA	Zhao Q.,et al."Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors".Science Bulletin 66.14(2021).