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Updated: Jan 9, 2026

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从文献中获得的迁移障碍的数据集,用于量化电池材料中的离子运输

Reshma Devi¹, Avaneesh Balasubramanian^1,2, Keith T Butler³

¹Department of Materials Engineering, Indian Institute of Science, Bengaluru, 560012, Karnataka, India.

Scientific data

|December 5, 2025

概括

这项研究为电池材料提供了621个离子迁移障碍 (E_m) 的数据集. 这些数据有助于开发机器学习模型,以加快新电极和固体电解质的发现.

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
电化学电化学电化学

背景情况:

电池性能依赖于离子迁移障碍 (E_m),这些障碍很难确定.
准确的机器学习 (ML) 模型用于预测E_m需要高质量的数据集.
加快新型电池材料的发现需要可靠的E_m数据.

研究的目的:

编制一套全面的文献报告的离子迁移障碍的数据集 (E_m).
促进机器学习模型的开发,用于预测E_m.
为了加速发明先进的电极和固体电解质材料用于电池.

主要方法:

从文献中收集了621个不同的E_m值.
利用密度函数理论 (DFT) 基于拉伸弹性带计算.
组织了443个组成和27个结构组的数据.

主要成果:

一个数据集,包含了各种电池电极和固体电解质化合物的621个E_m值.
包括每个化合物的结构信息和离子迁移途径.
数据提供在可访问的 .xlsx 和 JSON 格式.

结论:

数据集是科学界的重要资源.
能够开发精确的ML模型来预测E_m.
促进加速发现高性能电池材料.

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