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机器学习引导的添加剂的发现和优化,用于制备二氧化碳的催化剂

Ying Guo^1,2, Xinru He¹, Yuming Su¹

¹State Key Laboratory of Physical Chemistry of Solid Surfaces, iChem, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.

Journal of the American Chemical Society

|April 12, 2021

概括

机器学习加速了减少二氧化碳 (CO2RR) 的催化剂的发现. 这项研究使用ML优化铜催化剂,实现一氧化碳,酸和C2+产品的选择性.

科学领域:

电化学
材料科学
催化剂

背景情况:

催化剂的发现和优化对于化学转换至关重要.
机器学习 (ML) 提供了通过数据分析加速材料开发的强大工具.
铜 (Cu) 催化剂被广泛研究,用于将二氧化碳 (CO2RR) 减少到各种产品中.

研究的目的:

证明ML在电化学铜催化剂制剂中代性寻找添加剂的应用.
加速对特定CO2RR产品进行选择性的Cu催化剂的发现和优化.

主要方法:

使用了涉及实验测试,ML分析和预测/重新设计的代ML工作流程.
使用电化学沉积来制备各种添加剂的Cu催化剂.
基于实验数据的ML模型被训练并用于指导催化剂重新设计.

主要成果:

在ML驱动的代过程中,成功地确定了具有更好的CO2RR选择性的催化剂配方.
获得的催化剂对一氧化碳 (CO),酸 (HCOOH) 和C2+产品 (如乙烯,乙醇) 具有选择性.
由添加剂影响的催化剂形态和表面结构的微妙变化显著影响了CO2RR的选择性.

结论:

通过有效地从有限的实验数据中提取见解, ML 显著加速了催化剂的发现和优化.
开发的ML方法为设计对CO2RR的选择性Cu催化剂提供了可行的策略.
这项工作突显了将机器学习与材料科学中的实验努力相结合的潜力,以便快速开发催化剂.

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