S-4-18 The Most Comprehensive Database of Ionic Transport Characteristics to Date

The Most Comprehensive Database of Ionic Transport Characteristics to Date

Bing He1, Siqi Shi2,3,*

1 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;

2 School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China;

3 Materials Genome Institute, Shanghai University, Shanghai 200444, China

 

ABSTRACT: Transport characteristics of ionic conductors play a key role in the performance of electrochemical devices. However, due to the complexity of source, the data are scattered and unavailable in a retrievable format. Besides, most Materials Genome Initiative platforms do not contain the ion-transport properties. Voronoi Decomposition and bond-valence site energy (BVSE) methods attract a great attention owing to their efficient calculation time at the minute level. We built a database containing crystal structure information, ion migration channel connectivity and three-dimensional channel maps for over 29,000 inorganic compounds based on the combination of Voronoi decomposition and BVSE methods (Figure1). The calculations are a part of high-throughput investigations, undertaken within the framework of the Screening Platform for Solid Electrolytes (SPSE) (www.bmaterials.cn) which has been deployed to the National Supercomputer Center in Guangzhou (http://matgen.nscc-gz.cn/solidElectrolyte/).

 


Figure 1. The architecture of the ionic transport characteristics database.

Keywords: Transport characteristics; Ionic conductors; Database; Geometric analysis; Bond valence site energy

Brief Introduction of Speaker
Siqi Shi

Siqi Shi obtained his Ph.D. from Institute of Physics, CAS in 2004. After that, he joined National Institute of Advanced Industrial Science and Technology of Japan and Brown University of USA as a post-doctor until joining Shanghai University as a professor in early 2013. His current research interests focus on multiscale calculations of electrochemical energy storage materials and materials design and performance optimization using machine learning.