Flexible Atomic Catalysts for Full Spectrum Photocatalytic Production of Syngas via Machine Learning Acceleration

Utilizing solar energy for the photocatalytic CO2reduction reaction (CO2RR) is a potential solution to achieve virtuous carbon cycling and benefit the development of carbon neutrality. However, the full spectrum photocatalytic CO2RR is still challenging due to the limited light absorption and efficiency of photocatalysts to the solar energy. Meanwhile, although different products of photocatalytic CO2RR have been proposed, syngas with a mixture of CO and H2as one of the most important feedstocks for industrial applications still lack sufficient exploration. Accordingly, the developments of atomic catalysts (ACs) supported by novel semiconductors such as graphdiyne (GDY) becomes a promising solution for these challenges, which supplies tunable electronic structures and band gaps to enable broad light absorption, efficient photogeneration of electron-hole pairs, fast charge carrier transportations for photocatalysis. Therefore, based on the research motivations and our research foundations, we propose to use a theoretical exploration strategy to accelerate the development of ACs for efficient syngas production by full spectrum photocatalytic CO2RR.In this project, we will focus on three key research questions by the first-principles machine learning (FPML) methods and theoretical calculations including: (1) Investigate mechanisms of syngas formation by full spectrum photocatalytic CO2RR; (2) Identify the suitable combinations of metals and supports for syngas formation; and (3) Realize the fast screening of ACs for photocatalytic CO2RR towards syngas. For syngas production, we will build over 10,000 ACs models including single atomic catalysts (SACs) and dual atomic catalysts (DACs) on GDY with all metals in the periodic table. The intrinsic photocatalytic activities of ACs will be compared by electronic structures, band gaps, and optical properties. The loadings and compositions of metals in both SACs and DACs will be further optimized regarding the control of CO: H2ratio and long-term stability by reaction energy trends and molecular dynamic simulations. By parameterizing all the calculation data of this project and previous research, we will construct a large dataset to apply the FPML method for predictions of novel ACs with efficient generation of syngas with controllable compositions.The research output will significantly strengthen the understanding of photocatalytic CO2RR and supply guidance to the experiments to develop novel ACs for syngas production for broad applications. This research offers potential collaborations with frontier experiment groups in the future to achieve material advances, which accelerate technological developments and promote carbon neutrality in Hong Kong with long-term impacts.