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DOI	10.1039/c6ee02697d
	Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
	Sendek A.D.; Yang Q.; Cubuk E.D.; Duerloo K.-A.N.; Cui Y.; Reed E.J.
发表日期	2017
ISSN	17545692
起始页码	306
结束页码	320
卷号	10 期号:1
英文摘要	We present a new type of large-scale computational screening approach for identifying promising candidate materials for solid state electrolytes for lithium ion batteries that is capable of screening all known lithium containing solids. To be useful for batteries, high performance solid state electrolyte materials must satisfy many requirements at once, an optimization that is difficult to perform experimentally or with computationally expensive ab initio techniques. We first screen 12 831 lithium containing crystalline solids for those with high structural and chemical stability, low electronic conductivity, and low cost. We then develop a data-driven ionic conductivity classification model using logistic regression for identifying which candidate structures are likely to exhibit fast lithium conduction based on experimental measurements reported in the literature. The screening reduces the list of candidate materials from 12 831 down to 21 structures that show promise as electrolytes, few of which have been examined experimentally. We discover that none of our simple atomistic descriptor functions alone provide predictive power for ionic conductivity, but a multi-descriptor model can exhibit a useful degree of predictive power. We also find that screening for structural stability, chemical stability and low electronic conductivity eliminates 92.2% of all Li-containing materials and screening for high ionic conductivity eliminates a further 93.3% of the remainder. Our screening utilizes structures and electronic information contained in the Materials Project database. © The Royal Society of Chemistry 2017.
英文关键词	Chemical stability; Electric conductivity; Ionic conductivity; Lithium-ion batteries; Classification models; Computational structure; Electronic conductivity; Electronic information; Lithium ion conductors; Screening approaches; Solid-state electrolyte; Structural stabilities; Solid electrolytes; cation; database; electrical conductivity; electrolyte; lithium; optimization; performance assessment
语种	英语
来源期刊	Energy & Environmental Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/190577
作者单位	Department of Applied Physics, Stanford University, Stanford, CA 94305, United States; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, United States
推荐引用方式 GB/T 7714	Sendek A.D.,Yang Q.,Cubuk E.D.,et al. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials[J],2017,10(1).
APA	Sendek A.D.,Yang Q.,Cubuk E.D.,Duerloo K.-A.N.,Cui Y.,&Reed E.J..(2017).Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials.Energy & Environmental Science,10(1).
MLA	Sendek A.D.,et al."Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials".Energy & Environmental Science 10.1(2017).