Forecasting Catalytic Property‐Performance Correlations for CO₂ Hydrogenation to Methanol via Surrogate Machine Learning Framework

Abstract The hydrogenation of CO₂ to methanol has been studied by several researchers owing to its two significant merits, namely, mitigation of CO₂ emissions and production of renewable fuel. Consequently, numerous experiments have been reported for carbon‐neutral methanol synthesis over copper‐based catalysts over the past few years. However, it is very expensive and time‐consuming to always observe reaction changes with respect to input parameters (operating conditions and catalytic properties) experimentally. Herein, this study develops an ultrafast machine learning (ML) based framework to predict CO₂ conversion and methanol selectivity by comprehensive knowledge extraction (extensive database construction) from existing published literature. Among various ML algorithms, artificial neural networks (ANNs) exhibit best accuracy with R²_test values of 0.885 and 0.861 for CO₂ conversion and methanol selectivity, respectively. Subsequently, the efficacy and fidelity of developed neural networks are depicted by satisfactory performance (R² ≥ 0.80) for majority of unseen test datasets (literature‐based as well as the experimental dataset for Cu/ZnO/Al₂O₃ catalyst). Finally, this work concludes that this ML‐based concept allows to uncover catalytic property–performance correlations hidden in large body of existing experimental research. This offers an open and generic platform for a major breakthrough of ML in heterogeneous catalysis which can help in planning future experiments.