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基于机器学习的洞察力,用于高性能离子电池的基于的自我修复阳极

Mahta Moazzenzadeh¹, Mahmoud Samadpour²

¹Department of Physics, K. N. Toosi University of Technology, Tehran, Iran.

Scientific reports

|April 1, 2025

概括

机器学习加速了在离子电池中用于阳极的自我修复聚合物的发现. 这种方法确定了提高电池容量和寿命的关键聚合物特性,克服了传统的试错限制.

科学领域:

材料科学材料科学材料科学
电化学电化学电化学
计算化学计算化学

背景情况:

阳极为离子电池 (LIB) 提供更高的能量密度,但由于循环过程中体积膨胀而遭受容量降解.
自愈聚合物被探索为结合剂,以减轻阳极降解,但传统方法耗时且昂贵.

研究的目的:

采用机器学习来识别有效的自愈聚合物用于阳极.
阐明聚合物结构特征及其对阳极完整性和电池寿命的影响之间的关系.

主要方法:

使用了机器学习算法,包括随机森林,山脊,支持矢量机器和神经网络.
进行了SHAP分析,以确定特定的功能组和结构动机的影响.

主要成果:

神经网络在预测阳极的聚合物性能方面实现了96%的准确性.
确定以太功能组,键和双重相互连接的环对于保持电池容量至关重要.

结论:

机器学习为开发先进的电池材料提供了比实验选更有效的替代方案.
自愈聚合物的设计原则是为了提高基于的LIB的寿命和性能而建立的.

关键词:

离子电池是一种离子电池.机器学习机器学习神经网络的神经网络的神经网络自愈的聚合物聚合物阳极是一种阳极.

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