New Generalized Informatics Framework for Development of Large Scale Virtual Battery Material Databases

TL;DR

This paper presents a novel informatics framework that predicts capacities for over 100,000 spinel compounds, aiding in the selection of promising battery materials by bridging the gap between theoretical and experimental data.

Contribution

The paper introduces a scalable methodology for predicting battery material capacities and creating large, curated databases from experimental data, adaptable to various properties and structures.

Findings

Predicted capacities for over 100,000 spinel compounds.

Identified the 20 most promising candidate materials.

Demonstrated the methodology's adaptability to other properties and structures.

Abstract

In this paper, we introduce an approach for the prediction of capacity for over 100,000 spinel compounds relevant for battery materials, from which we propose the 20 most promising candidate materials. In the design of batteries, selecting the proper material is difficult because there are so many metrics to consider, including capacity which is a fundamental engineering property. Using reported experimental data as our starting point, we demonstrate how we can build a dataset that provides a guide for the selection of battery materials. Although we focus on capacity of Li based spinel structures for electrode materials relevant for usage in batteries, the methodology developed and demonstrated here can be adapted to other properties, structures, and site occupancies. Further, theoretical capacity is often used as a guideline for material design of battery materials. In this paper, we show how this is insufficient for representing experimental measurements, while our methodology closes this gap and provides an accurate computational representation of experimental data.