arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.00586cond-mat.mtrl-sci
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基于机器学习的锌离子电池正极材料预测

Machine Learning Enabled Prediction of Cathode Materials for Zn ion Batteries

Linming Zhou, Archie Mingze Yao, Yongjun Wu, Ziyi Hu, Yuhui Huang, Zijian Hong

摘要:

水电解质锌二次电池具有成本低、体积容量大、环境友好、安全性高等优点,被认为是一种很有前途的替代储能技术。然而,缺乏可靠的阴极材料在很大程度上限制了它们的应用。在此,我们开发了一种基于机器学习(ML)的方法来预测具有高容量(>150 mAh/g)和高电压(>0.5V)的阴极。我们从Materials Project数据库中筛选了超过130,000种无机材料,并将基于晶体图卷积神经网络(CGCNN)的ML方法应用于AFLOW数据库中的数据。这两者的结合不仅可以筛选出与实验数据相匹配的阴极材料,而且还可以预测新的有希望的候选材料,以供进一步的实验验证。我们希望这项研究能够激发人们对基于ML的电池材料发现先进理论工具的进一步兴趣。

英文摘要:

Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety. However, a lack of reliable cathode materials has largely pledged their applications. Herein, we developed a machine learning (ML) based approach to predict cathodes with high capacity (>150 mAh/g) and high voltage (>0.5V). We screened over ~130,000 inorganic materials from the Materials Project database and applied the crystal graph convolutional neural network (CGCNN) based ML approach with data from the AFLOW database. The combination of these two could not only screen cathode materials that match well with the experimental data but also predict new promising candidates for further experimental validations. We hope this study could spur further interests in ML-based advanced theoretical tools for battery materials discovery.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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