Quantitative description on structure–property relationships of Li-ion battery materials for high-throughput computations

Abstract

Li-ion batteries are a key technology for addressing the global challenge of clean renewable energy and environment pollution. Their contemporary applications, for portable electronic devices, electric vehicles, and large-scale power grids, stimulate the development of high-performance battery materials with high energy density, high power, good safety, and long lifetime. High-throughput calculations provide a practical strategy to discover new battery materials and optimize currently known material performances. Most cathode materials screened by the previous high-throughput calculations cannot meet the requirement of practical applications because only capacity, voltage and volume change of bulk were considered. It is important to include more structure–property relationships, such as point defects, surface and interface, doping and metal-mixture and nanosize effects, in high-throughput calculations. In this review, we established quantitative description of structure–property relationships in Li-ion battery materials by the intrinsic bulk parameters, which can be applied in future high-throughput calculations to screen Li-ion battery materials. Based on these parameterized structure–property relationships, a possible high-throughput computational screening flow path is proposed to obtain high-performance battery materials.

Graphical abstract

In this review, we discuss some quantitative descriptions of structure–property relationships by the intrinsic bulk parameters, which can be applied in the future high-throughput computational screening to obtain high-performance Li-ion battery materials.

Keywords:

• Li-ion battery materials
• high throughput screening
• quantitative description
• structure-property

Classification:

• 206 Energy conversion / transport / storage / recovery
• 404 Materials informatics / Genomics

Acknowledgements

This work is financially supported by National Natural Science Foundation of Chinese, NSFC [51432010, 21573272, 51622207, 51372228 and U1630134], and the research grant [16DZ2260600] from Science and Technology Commission of Shanghai Municipality.