可解释的机器学习辅助选矿氧化物
Jie Zhao1, Xiaoyan Wang2, Haobo Li3
1College of Chemical Engineering, Nanjing Tech University Nanjing Jiangsu 211816 China j.zhao1@njtech.edu.cn.
RSC advances
|January 29, 2024
概括
机器学习模型有效地识别稳定的矿氧化物用于能源应用. 这种方法通过预测凸船体上方的热力学稳定性和能量来加速新材料的发现.
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
- 机器学习 机器学习
背景情况:
- 矿氧化物对于储能和转换技术至关重要.
- 传统的查方法 (实验和DFT) 耗时且昂贵.
研究的目的:
- 开发可解释的机器学习模型,用于预测矿氧化物稳定性.
- 为了加速从庞大的虚拟组合图书馆中识别稳定的矿氧化物.
主要方法:
- 构建了分类和回归模型,以预测凸船体 (Eh) 上方的热力学稳定性和能量.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
- 利用了诸如最高占成的分子轨道能量,弹性模量和离子半径等特征.
主要成果:
- 一个分类模型实现了高精度 (0.919),精度 (0.937),F1得分 (0.932),和回忆 (0.935).
- 从超过110万个虚拟组合中成功选了超过682,000个稳定的矿氧化物.
- 一个回归模型预测了EH值,确定系数为0.916,与DFT计算有很好的一致性.
结论:
- 可解释机器学习为矿氧化物发现的传统方法提供了有效的替代方案.
- 关键材料描述符,如最高占成的分子轨道能量和弹性模量,对于稳定性预测至关重要.
- 开发的模型大大加快了为能源应用寻找新矿材料的搜索速度.
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