CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

Abstract The emission of CO 2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon‐neutrality by 2050, CO 2 capture and utilization is crucial. The efficient conversion of CO 2 to C 5+ gasoline and aromatics remains elusive mainly due to CO 2 thermodynamic stability and the high energy barrier of the C−C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.