快离子导体
电负性
理论(学习稳定性)
计算机科学
材料科学
晶体结构预测
排名(信息检索)
拓扑(电路)
机器学习
化学
数学
物理化学
晶体结构
结晶学
电解质
有机化学
组合数学
电极
作者
Bin Ouyang,Jingyang Wang,Tanjin He,Christopher J. Bartel,Haoyan Huo,Yan Wang,Valentina Lacivita,Haegyeom Kim,Gerbrand Ceder
标识
DOI:10.1038/s41467-021-26006-3
摘要
Abstract In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure.
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