在高透合金催化剂上解脱CO2的减少反应中间体:一种可解释的机器学习方法来建立缩放关系
Diptendu Roy1, Shyama Charan Mandal1,2,3, Amitabha Das1
1Department of Chemistry, Indian Institute of Technology Indore, Indore, 453552, India.
Chemistry (Weinheim an der Bergstrasse, Germany)
|November 15, 2023
概括
本研究引入了一种可解释的机器学习 (ML) 方法,用于预测高合金 (HEA) 表面上二氧化碳减排反应 (CO2 RR) 中间体的缩放关系,从而改进催化剂设计.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 在复杂表面,如高合金 (HEAs) 上建立反应中间体的缩放关系至关重要,但具有挑战性.
- 目前在催化中的机器学习 (ML) 模型往往缺乏可解释性,阻碍了机械理解和理性设计.
研究的目的:
- 开发一种可解释的ML方法,用于在HEA表面上建立二氧化碳还原反应 (CO2 RR) 中间体之间的缩放关系.
- 增强对复杂材料系统上的催化过程的理解和可预测性.
主要方法:
- 设计了一个可解释的ML框架,使用本地可解释的模型不可知解释 (LIME),累积的本地效应 (ALE) 和转换特征重要性 (PFI).
- 在基于CuCoNiZnMg和CuCoNiZnSn的HEA数据上反复应用和验证了ML方法.
- 采用黑子模型来解释全球和本地吸附能量.
主要成果:
- 通过使用高精度的HCO训练数据 (MAE:分别为0.24 eV和0.23 eV) 成功预测了*H2CO和*H3CO中间体的吸附能量.
- 从*H训练数据 (MAE:0.32 eV) 中准确预测了*O中间体的吸附能量.
- 在复杂的HEA表面上证明了可解释ML方法的有效性.
结论:
- 提出的可解释的ML方法成功地建立了HEAs上的CO2 RR中间体的缩放关系.
- 这种方法提供了一种途径,可以将ML的范式转变为催化,以提高可解释性和预测能力.
- 这些发现为更合理地设计二氧化碳转化催化剂铺平了道路.
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