DOI: https://doi.org/10.29363/nanoge.interect.2021.010
Publication date: 10th November 2021
Machine learning (ML) has proven a powerful tool for accelerating the computational characterization of energy materials. There is a growing number of case studies identifying descriptors of catalytic performance using ML instead of physical intuition. ML 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 small set of experimental data.1 The approach is based on combining a complex ML model trained on a computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities. 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.2 All data and models are made publicly available. To promote Open Science, we also formulated guidelines for the publication of ML models for chemistry that aim at transparency and reproducibility.3
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