Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO2 on CuPt/TiO2

The photoconversion of CO2 to hydrocarbons is a sustainable route for its transformation into value-added compounds, which is crucial to mitigating energy and climate crises. CuPt nanoparticles on TiO2 surfaces have been reported to show promising photoconversion efficiencies. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO2 systems is vital. Here, we employ ab initio calculations, machine learning, and photocatalysis experiments to understand the photocatalytic reduction of CO2 on CuPt/TiO2. We explore the configurational space of the CO2@CuPt/TiO2 systems and examine their structures and energetics. We find that the CuPt/TiO2 interface plays a key role in determining CO2 activation and, thus, the conversion to hydrocarbons. The interface stabilizes *CO and other intermediates containing CH groups, thus facilitating a higher activity and selectivity for methane. A bias-corrected machine-learning interatomic potential trained on density functional theory data enables the efficient exploration of the potential energy surfaces of numerous CO2@CuPt/TiO2 configurations using basin-hopping Monte Carlo simulations, greatly accelerating the study of these photocatalyst systems. Our simulations show that CO2 preferentially adsorbs at the interface, with a C atom bonded to a Pt site and one O atom occupying an O-vacancy site. The interface also promotes the formation of *CH and *CH2 intermediates. For confirmation, we synthesize CuPt/TiO2 samples with various compositions, analyze their morphologies and compositions using scanning electron microscopy and energy-dispersive X-ray spectroscopy, and measure their photocatalytic activity. Our computational and experimental findings qualitatively agree and highlight the importance of the interface design for the selective conversion of CO2 to hydrocarbons.