School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
Sci Data. 2020 May 21;7(1):151. doi: 10.1038/s41597-020-0474-y.
The combination of a materials database with high-throughput ion-transport calculations is an effective approach to screen for promising solid electrolytes. However, automating the complicated preprocessing involved in currently widely used ion-transport characterization algorithms, such as the first-principles nudged elastic band (FP-NEB) method, remains challenging. Here, we report on high-throughput screening platform for solid electrolytes (SPSE) that integrates a materials database with hierarchical ion-transport calculations realized by implementing empirical algorithms to assist in FP-NEB completing automatic calculation. We first preliminarily screen candidates and determine the approximate ion-transport paths using empirical both geometric analysis and the bond valence site energy method. A chain of images are then automatically generated along these paths for accurate FP-NEB calculation. In addition, an open web interface is actualized to enable access to the SPSE database, thereby facilitating machine learning. This interactive platform provides a workflow toward high-throughput screening for future discovery and design of promising solid electrolytes and the SPSE database is based on the FAIR principles for the benefit of the broad research community.
将材料数据库与高通量离子输运计算相结合，是筛选有前途的固体电解质的有效方法。然而，自动化目前广泛使用的离子输运特性算法（如第一性原理力逼弹性带（FP-NEB）方法）所涉及的复杂预处理仍然具有挑战性。在此，我们报告了一种用于固体电解质的高通量筛选平台（SPSE），它将材料数据库与通过实施经验算法实现的分层离子输运计算相结合，以辅助 FP-NEB 完成自动计算。我们首先使用经验的几何分析和键价位点能方法对候选物进行初步筛选，并确定近似的离子输运路径。然后，沿着这些路径自动生成一系列图像，以进行准确的 FP-NEB 计算。此外，还实现了一个开放的网络界面，以实现对 SPSE 数据库的访问，从而便于机器学习。该交互式平台为未来有前途的固体电解质的高通量筛选和设计提供了一种工作流程，并且 SPSE 数据库基于 FAIR 原则，以造福广大研究社区。
