journal article·2021-01

A highly efficient and informative method to identify ion transport networks in fast ion conductors

Bing He, Penghui Mi, Anjiang Ye, Shuting Chi, Yao Jiao, Liwen Zhang, Bowei Pu, Zheyi Zou, Wenqing Zhang, Maxim Avdeev, Stefan Adams, Jingtai Zhao, Siqi Shi

Acta Materialia· Volume 203
Publisher page

Abstract

Abstract High-throughput analysis of the ion transport pathways is critical for screening fast ion conductors. Currently, empirical methods, such as the geometric analysis and bond valence site energy (BVSE) methods, are respectively used for the task. Geometric analysis method can only extract geometric and topological pathway properties without considering the interatomic interactions, while the BVSE method alone does not yield a geometric classification of the sites and interstices forming the pathway. Herein, we propose a highly efficient and informative method to identify interstices and connecting segments constructing an ion transport network by combining topological pathway network and BVSE landscape, which enables to obtain both the geometry and energy profiles of nonequivalent ion transport pathways between adjacent lattice sites. These pathways can be further used as the input for first-principles nudged elastic band calculations with automatically generated chains of images. By performing high-throughput screening of 48,321 Li-, Na-, Mg- and Al-containing ionic compounds from the Inorganic Crystal Structure Database based on the filter combining geometric analysis and BVSE methods, we obtain 1,270 compounds with connected ionic migration pathways of suitable sizes and low migration energy barriers, which include both previously reported fast ion conductors, and new promising materials to be explored further.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Machine Learning in Materials Science · Advanced Battery Materials and Technologies · Advancements in Battery Materials

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