Efficient Screening of Metal Promoters of Pt Catalysts for C-H Bond Activation in Propane Dehydrogenation from a Combined First-Principles Calculations and Machine-Learning Study

Abstract

Platinum-based materials are the most widely used catalysts in propane direct dehydrogenation, which could achieve a balanced activity between both propane conversion and propene formation. One of the core issues of Pt catalysts is how to efficiently activate the strong C-H bond. It has been suggested that adding second metal promoters could greatly solve this problem. In the current work, first-principles calculations combined with machine learning are performed in order to obtain the most promising metal promoters and identify key descriptors for control performance. The combination of three different modes of adding metal promoters and two ratios between promoters and platinum sufficiently describes the system under investigation. The activity of propane activation and the formation of propene are reflected by the increase or decrease of the adsorption energy and C-H bond activation of propane and propene after the addition of promoters. The data of adsorption energy and kinetic barriers from first-principles calculations are streamed into five machine-learning methods including gradient boosting regressor (GBR), K neighbors regressor (KNR), random forest regressor (RFR), and AdaBoost regressor (ABR) together with the sure independence screening and sparsifying operator (SISSO). The metrics (RMSE and R²) from different methods indicated that GBR and SISSO have the most optimal performance. Furthermore, it is found that some descriptors derived from the intrinsic properties of metal promoters can determine their properties. In the end, Pt₃Mo is identified as the most active catalyst. The present work not only provides a solid foundation for optimizing Pt catalysts but also provides a clear roadmap to screen metal alloy catalysts.