Effect of Feature Selection on Data-Driven Prediction of Catalyst Performance: A Case Study on Methanol Formation from Thermocatalytic CO2 Hydrogenation on Cu-Based Catalysts

CO 2 conversion to methanol via thermocatalytic hydrogenation is one of the viable alternatives to address climate change problem while producing a valuable industrial product. However, this comes with a challenge, i.e., predicting the performance of catalytic systems. In this work, we present a data-driven study to predict the performance of Cu-based catalyst based on a compiled dataset consisting of 15 features obtained from experiment data. Furthermore, we implement feature selection techniques such as univariate, RFE, and XGBoost to investigate how the performance of the prediction model changes with varied number of features. The results show that features selected by RFE method yields the best performance with 7 number of features, capable of even outperforms the baseline model in terms of accuracy and feasibilty. This suggests that feature selection technique is relevant in terms of constructing a machine learning model for predicting methanol production via CO 2 thermocatalytic hydrogenation.