一个知识-数据双驱动框架,用于预测可充电电池电解质的分子特性
Yu-Chen Gao1, Yu-Hang Yuan1, Suozhi Huang2
1Tsinghua Center for Green Chemical Engineering Electrification (CCEE), Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
Angewandte Chemie (International ed. in English)
|October 11, 2024
概括
一个新的框架,基于知识的电解质属性预测集成 (KPI),准确地预测电解质属性,用于更安全,更广泛的温度范围的电池. 这种人工智能驱动的方法加速了先进的储能解决方案的开发.
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:
- 对可充电电池的日益增长的需求需要更广泛的操作温度范围和更高的安全性.
- 精确预测电解质分子特性,如点 (MP),沸点 (BP) 和闪点 (FP),对于高效的电池开发至关重要.
研究的目的:
- 开发一个知识-数据双驱动的框架,用于预测电解质的分子特性.
- 为了加速新型电解质分子的发现,用于高性能电池.
主要方法:
- 基于知识的电解质属性预测集成 (KPI) 框架收集和结构分子数据.
- 可解释的机器学习模型分析结构-属性关系.
- 发现的知识被嵌入到财产预测模型中.
主要成果:
- 实现了低平均绝对误差10.4K (MP),4.6K (BP) 和4.8K (FP).
- 在20个数据集中的18个数据集中达到最先进的性能.
- 通过使用高通量选,确定了15个和14个有前途的分子,用于广泛的温度范围和高安全性电池.
结论:
- 该KPI框架准确地预测了分子性质,并增强了对结构-性质关系的理解.
- KPI有效地将人工智能与电池材料发现的领域知识集成在一起.
- 这种方法加速了更安全,高性能可充电电池的开发.
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