Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

• Published in: ACS catalysis Vol. 10; no. 16; pp. 9438 - 9444
• Main Authors: Artrith, Nongnuch, Lin, Zhexi, Chen, Jingguang G
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 21.08.2020; American Chemical Society (ACS)
• Subjects: bimetallic catalysts; density-functional theory; Ethanol reforming; Gaussian process regression; INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; machine learning; random forest regression; transition states; bimetallic catalysts; transition states; random forest regression; ethanol reforming; density-functional theory; Gaussian process regression; machine learning
• ISSN: 2155-5435; 2155-5435
• DOI: 10.1021/acscatal.0c02089

Machine learning is ideally suited for the pattern detection in large uniform data sets, but consistent experimental data sets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on a computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond-scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM–Pt–Pt(111) and Pt–TM–Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. The approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of catalytic materials.