Accelerating the Prediction of g‐C <sub>3</sub> N <sub>4</sub> ‐Supported Dual‐Atom Catalysts for Photocatalytic CO <sub>2</sub> Reduction to CO and HCOOH: A Machine Learning and DFT Combined Approach
Yingmei Bian, Yanxin Wang, Zijun Yang, Zexiang Yin, Heng Zhao, Yuan Liu, Hainan Shi, Yaqiong Su, Yida Deng, Haozhi Wang
Abstract
Abstract Dual‐atom catalysts (DACs) show promise for photocatalytic carbon dioxide (CO 2 ) reduction due to their high atom utilization and synergistic effects. However, finding efficient combinations is challenging because of the large number of possibilities. In this study, a high‐throughput screening of TM1TM2@g‐C 3 N 4 catalysts is conducted using density functional theory (DFT) and machine learning (ML) to identify promising candidates for CO 2 photoreduction. The results reveal that the ML algorithm can successfully achieve the relationship between the descriptors of the DACs and the limiting potentials ( U L ) of CO and HCOOH products. Among four ML models, the feedforward neural network (FNN) achieves the highest accuracy. The ML model successfully predicts the limiting potentials ( U L ) and screens RuHf@g‐C 3 N 4 ( U L = −0.72 V) and VV@g‐C 3 N 4 ( U L = −0.79 V) as promising CO producing catalysts, and MoMo@g‐C 3 N 4 ( U L = −0.28 V) and CrMo@g‐C 3 N 4 ( U L = −0.32 V) as efficient HCOOH producing catalysts. DFT validation shows low average errors (−0.05 eV for CO, 0.02 eV for HCOOH). Compared to pure DFT, the FNN model reduces screening time by 56% (CO) and 42% (HCOOH). The ML framework not only successfully screens highly promising catalysts but also provides a solid theoretical basis for the subsequent experimental synthesis for energy conversion.