Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data-Driven Design

The solar-energy-driven photoreduction of CO2 has recently emerged as a promising approach to directly transform CO2 into valuable energy sources under mild conditions. As a clean-burning fuel and drop-in replacement for natural gas, CH4 is an ideal product of CO2 photoreduction, but the development of highly active and selective semiconductor-based photocatalysts for this important transformation remains challenging. Hence, significant efforts have been made in the search for active, selective, stable, and sustainable photocatalysts. In this review, recent applications of cutting-edge experimental and computational materials design strategies toward the discovery of novel catalysts for CO2 photocatalytic conversion to CH4 are systematically summarized. First, insights into effective experimental catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet engineering, and single atoms, are presented. Then, data-driven photocatalyst design spanning density functional theory (DFT) simulations, high-throughput computational screening, and machine learning (ML) is presented through a step-by-step introduction. The combination of DFT, ML, and experiments is emphasized as a powerful solution for accelerating the discovery of novel catalysts for photocatalytic reduction of CO2. Last, challenges and perspectives concerning the interplay between experiments and data-driven rational design strategies for the industrialization of large-scale CO2 photoreduction technologies are described.