Interpretable learning of voltage for electrode design of multivalent metal-ion batteries

TL;DR

This paper introduces an interpretable deep learning model for predicting electrode voltages in multivalent metal-ion batteries, achieving high accuracy with small datasets and providing insights into key atomic features influencing voltage.

Contribution

The study develops a novel, explainable deep learning approach for voltage prediction in multivalent MIBs, effective with limited data and automatically identifying important atomic features.

Findings

Model outperforms traditional machine learning in small dataset regimes.

Automatically extracts covalent radius as key feature for voltage prediction.

Provides a publicly available online toolkit for battery research.

Abstract

Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design of current commercial Li-ion batteries. However, its practical utility for multivalent metal-ion batteries (MIBs), the most promising future solution of large-scale energy storage, is limited due to the scarce MIB data availability and poor DL model interpretability. Here, we develop an interpretable DL model as an effective and accurate method for learning electrode voltages of multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at small dataset limits (150~500). Using the experimental results as validation, our model is much more accurate than machine-learning models which usually are better than DL in the small dataset regime. Besides the high accuracy, our feature-engineering-free DL model is explainable, which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network. The presented model potentially accelerates the design and optimization of multivalent MIB materials with fewer data and less domain-knowledge restriction, and is implemented into a publicly available online tool kit in http://batteries.2dmatpedia.org/ for the battery community.