Voltage Mining for (De)lithiation-stabilized Cathodes and a Machine Learning Model for Li-ion Cathode Voltage

TL;DR

This paper analyzes voltage characteristics of cathodes in lithium-ion batteries, revealing design principles for high-voltage cathodes and introducing a machine learning model for voltage prediction based on chemical formulas.

Contribution

It combines extensive voltage data analysis with the development of a novel machine learning model for accurate voltage prediction from chemical formulas.

Findings

Charged-state cathodes tend to have lower voltages than discharged-state cathodes.

High-voltage cathodes favor later Period 4 transition metals and electronegative anions.

The ML model outperforms existing composition-based models like Roost and CrabNet.

Abstract

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyaion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulae is constructed, and shows state-of-the-art performance when compared to two established composition-based ML models for materials properties predictions, Roost and CrabNet.