在丰富的NCM材料中通过机器学习和化学意识的推算来进行不确定性量化初级粒子大小预测
Benediktus Madika1, Chaeyul Kang1, JooSung Shim1
1Department of Materials Science and Engineering, KAIST, Daejeon, 34141, South Korea.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 8, 2025
概括
机器学习使用数据推算准确预测丰富的NCM阴极颗粒大小. 烧结条件,而不是成分,是这些先进的电池材料中微观结构演化的关键驱动因素.
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
- 数据科学数据科学数据科学
背景情况:
- 富含的-- (富含NCM) 材料对于先进的离子电池至关重要.
- 这些材料的电化学性能高度依赖于初级粒子大小.
- 使用机器学习来预测粒子大小,由于文献数据不完整而受到阻碍.
研究的目的:
- 增强机器学习模型来预测丰富的NCM初级粒子大小.
- 评估数据归算技术对预测准确性和不确定性量化的影响.
- 确定影响丰富NCM材料中的粒子大小的关键因素.
主要方法:
- 应用推算方法 (MatImpute,KNN,MICE,Mean) 来完成数据集.
- 训练了一种自然梯度提升 (NGBoost) 模型,用于用不确定性量化进行粒子大小预测.
- 评估了两个培训策略:包括和不包括假定的目标值.
主要成果:
- 基于MatImpute的NGBoost模型实现了最高的精度 (测试R2为0.866) 和最低的校准误差 (0.133).
- 第二次烧结温度和第一次烧结时间被确定为主要的预测因素.
- 实验验证证证实了高预测准确性,大多数值在0.13微米的测量范围内.
结论:
- 强大的数据归算和不确定性量化显著改善了基于ML的丰富的NCM材料的颗粒大小预测.
- 烧结条件,而不是材料固态度,是微观结构进化的主要决定因素.
- 这种方法为更可靠地预测和优化电池阴极材料提供了一条途径.
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