用机器学习快速筛选出120种高性能合金负极材料
Machine learning assisted screening of metal binary alloys for anode materials
- 基于CGCNN模型结合海量数据库数据预测性能
- 发现约120种低电位高比容量的合金负极材料
- 适用于锂钠钾锌镁钙铝等多种电池体系
在快速发展的电池领域，合金负极材料因其优异电化学性能备受关注。传统筛选方法效率低、耗时长。本研究提出一种机器学习辅助策略，整合来自MP和AFLOW数据库的数万种合金成分与性能数据，利用CGCNN模型准确预测合金负极的电位和比容量，并通过实验数据验证。该方法成功识别出约120种低电位、高比容量的合金负极材料，适用于锂、钠、钾、锌、镁、钙、铝等多种电池体系。该方法不仅加速了负极材料的筛选进程，也推动了储能材料研究与技术创新。
原文摘要 · Abstract (English)
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.
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