High-Throughput Computation of Li-based Battery Material Databases: Chemistry-Processing-Property Relationships

TL;DR

This study enhances the modeling of Li-based spinel battery materials by integrating processing parameters, significantly expanding the design space and aiding in the discovery of new high-capacity battery materials.

Contribution

It introduces a novel approach to incorporate processing effects into property modeling, increasing the potential combinations from 125,000 to two million for battery material design.

Findings

Increased the number of potential material combinations to two million.

Developed a new method to track non-linear processing-property relationships.

Provided a tool to guide experimental battery material discovery.

Abstract

In this paper, we incorporate processing into the data driven property modeling of Li-based spinel battery materials. When considering spinel compounds of the form LiMe2O4 with Me as a metal or metals, there are 125,000 possible combinations assuming a maximum of three metallic elements. In this work, we focus on capacity for our predicted property, and increase the number of possible combinations to two million by incorporating processing into the modeling. Due to the non-linear relationship between processing and property, as well as ensuring proper sensitivity, a new approach which tracks the change with changing processing is introduced. This work provides an invaluable tool for guiding the next generation of battery experiments, providing a significant narrowing of the infinite design space.