机器学习辅助的高捐赠人数电解质添加选,用于制造无树的水性离子电池

Haoran Luo1, Qianzhi Gou1,2, Yujie Zheng1

  • 1National Innovation Center for Industry-Education Integration of Energy Storage, MOE Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, CQU-NUS Renewable Energy Materials & Devices Joint Laboratory, College of Energy & Power Engineering, Chongqing University, Chongqing 400044, China.

ACS nano
|January 8, 2025
PubMed
概括

机器学习加速了为无树酸盐水性离子电池 (AZIB) 发明电解质添加剂的发现. 预测古特曼捐赠人数 (DN值) 的模型将异醇 (IPA) 确定为一种高性能添加剂,增强电池的稳定性和寿命.