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

TL;DR

This study combines machine learning and first-principles calculations to predict activity and selectivity of bimetallic catalysts for ethanol reforming, enabling efficient catalyst discovery from limited experimental data.

Contribution

It introduces a hybrid modeling approach that integrates complex ML models with simple regression to analyze small datasets and predict promising catalyst compositions.

Findings

Identified key C-C bond scission reactions in ethanol reforming.

Screened and proposed four promising bimetallic catalyst compositions.

Demonstrated the method's general applicability to catalyst discovery.

Abstract

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets 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.