Machine learning assisted screening of metal binary alloys for anode materials

TL;DR

This paper presents a machine learning approach using graph convolutional neural networks to rapidly screen and identify promising alloy anode materials for various batteries, significantly accelerating materials discovery.

Contribution

It introduces a large dataset and a CGCNN model for predicting alloy anode properties, improving efficiency over traditional screening methods.

Findings

Identified ~120 promising alloy anodes for multiple battery types.

Validated model predictions against experimental data.

Streamlined the screening process for battery materials.

Abstract

In the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a machine learning-assisted strategy to expedite the discovery and optimization of these materials. We compiled a vast dataset from the MP and AFLOW databases, encompassing tens of thousands of alloy compositions and properties. Utilizing a CGCNN, we accurately predicted the potential and specific capacity of alloy anodes, validated against experimental data. This approach identified approximately 120 low potential and high specific capacity alloy anodes suitable for various battery systems including Li, Na, K, Zn, Mg, Ca, and Al-based. Our method not only streamlines the screening of battery anode materials but also propels the advancement of battery material research and innovation in energy storage technology.