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高稳定的氧化碳全固态电池通过机器学习加速选氧化物和硫化物电解质

Duo Yang¹, Pengchong Xu¹, Changgui Xu¹

¹College of Mechanical & Electrical Engineering Wenzhou University, Wenzhou 325035, PR China.

Journal of colloid and interface science

|July 31, 2024

概括

本研究引入了一种机器学习和第一原则方法,以有效地选稳定的氧化碳全固态电池的材料. 该方法加速了为先进的电池开发寻找有前途的阳极-固体电解质接口的发现.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
电化学电化学电化学

背景情况:

开发高性能电池材料的传统方法是缓慢和低效的.
确定最佳的阳极-固态电解质接口对于稳定的全固态电池至关重要.

研究的目的:

开发一个计算策略,以高效高通量选氧化碳全固态电池的电解质.
为了加速新型阳极-固体电解质接口的发现.

主要方法:

结合机器学习 (ML) 和第一原则计算.
使用了诸如界面粘附和形成能量之类的新描述符.
采用梯度增强回归树模型进行界面能量预测.

主要成果:

第一原则计算揭示了材料类型和兴奋剂对界面兼容性的影响.
该ML模型在预测接口形成能量方面实现了高精度 (R2=0.99) 和可靠性.
通过低成本描述器,显著减少了对广泛培训数据的需求.

结论:

提出的计算策略使氧化物和硫化物电解质的有效选成为可能.
发现了几个新的,稳定的阳极-固体电解质接口对,用于实验验证.
这种方法加速了高性能全固态电池的开发.

关键词:

所有固态电池都是固态电池.第一个原则.接口稳定性接口稳定性机器学习是机器学习.材料设计材料设计

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