Machine learning assisted screening of metal binary alloys for anode materials
This work addresses the need for faster discovery of alloy anode materials for various battery systems, representing an incremental improvement over traditional screening methods.
The researchers tackled the inefficient screening of metal binary alloys for battery anode materials by developing a machine learning-assisted strategy using a CGCNN on a large dataset, which identified about 120 low potential and high specific capacity alloy anodes validated against experimental data.
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.