机器学习辅助筛选金属二元合金阳极材料

Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong
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引用次数: 0

摘要

在充满活力且飞速发展的电池领域,合金负极材料因其卓越的电化学性能而成为焦点。传统的筛选方法效率低、耗时长。我们的研究引入了机器学习辅助策略,以加快这些材料的发现和优化。我们从 MP 和 AFLOW 数据库中汇编了一个庞大的数据集,其中包含数以万计的合金成分和特性。利用 CGCNN,我们准确预测了合金阳极的电势和比容量,并根据实验数据进行了验证。这种方法确定了约 120 种适用于各种电池系统的低电位、高比容量合金阳极,包括锂基、镍基、钾基、锌基、镁基、钙基和铝基阳极。我们的方法不仅简化了电池阳极材料的筛选过程,还推动了电池材料研究和储能技术的创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning assisted screening of metal binary alloys for anode materials
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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