# Machine Learning the Voltage of Electrode Materials in Metal-ion Batteries

## TL;DR

This paper presents a machine learning tool that predicts electrode voltages in metal-ion batteries, enabling rapid screening of new materials and reducing reliance on computationally intensive methods.

## Contribution

The authors develop a web-based ML tool using neural networks, SVM, and kernel ridge regression to predict electrode voltages, proposing nearly 5,000 new candidate materials.

## Key findings

- ML models accurately predict voltages across test sets.
- The tool can generate voltage profiles comparable to DFT calculations.
- Approximately 5,000 candidate electrode materials for Na- and K-ion batteries identified.

## Abstract

Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool (http://se.cmich.edu/batteries) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project Database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5,000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal-ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.