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使用HOMO描述器的数据驱动增材发现通过界面动力学工程实现了耐用水性电池

Shaohua Han¹, Yankai Zheng², Xu Zhang²

¹School of Materials Science and Engineering, Hunan Provincial Key Laboratory of Electronic Packaging and Advanced Functional Materials, Central South University, Changsha, 410083, China.

Advanced materials (Deerfield Beach, Fla.)

|August 30, 2025

概括

机器学习加速了稳定水性离子电池的添加剂的发现. 一个新的预测模型确定了4-二甲胺,它可以抑制树突和寄生反应,从而使电池性能持久.

科学领域:

材料科学
电化学
计算化学

背景情况:

水性离子电池 (AZIB) 面临着树突生长和寄生反应等挑战,限制了它们的界面稳定性和电荷转移.
这些问题源于接口不稳定性和不受控制的电化学过程.
开发有效的添加查策略对于推进AZIB技术至关重要.

研究的目的:

开发一种机器学习加速的策略,用于识别AZIB的有效电解质添加剂.
建立一个预测框架,将分子特性 (HOMO能量) 与Zn2+在接口上的行为联系起来.
发现新的添加剂,增强界面稳定性并抑制AZIB的树突形成.

主要方法:

利用在分子数据集上训练的可解释机器学习模型 (自适应提升) 预测最高占成的分子轨道 (HOMO) 能量水平.
与Zn2+离子的吸附和还原行为相关联的HOMO能量水平.
使用预测模型选潜在的添加剂,并通过实验验证确定候选剂的性能.

主要成果:

机器学习模型在HOMO预测中获得了高准确度 (R2 = 0.8032,MSE = 0.2977).
通过调节界面电荷转移和离子沉积动力学,4- dimethylaminopyridine被确定为一种高性能添加剂,有效抑制树突.
该添加剂还通过排除水分子来抑制寄生反应,从而在1600个循环中获得99.85%的平均库伦比效率.
一个NaV3O8·1.5H2OZn囊细胞在250个循环后显示稳定循环70天,容量保持73%.

结论:

开创了机器学习与接口电化学的整合,用于AZIB的增材发现.
证明了合理的添加剂选和电解质设计的通用方法.
建立了在水性电池系统中实现无石金属阳极的新范式.

关键词:

一个HOMO描述符水性电池数据驱动的添加物发现接口动力学工程

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