DOI: 10.1016/j.scib.2021.04.029
论文题名: Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors
作者: Zhao Q. ; Avdeev M. ; Chen L. ; Shi S.
刊名: Science Bulletin
ISSN: 20959273
出版年: 2021
卷: 66, 期: 14 起始页码: 1401
结束页码: 1408
语种: 英语
中文关键词: 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
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 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/170518
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Science Bulletin,2021-01-01,66(14)