What type of study is this?

This is a In Vitro Study study.

August 20, 2025

Accelerating the Prediction of g‐C3N4‐Supported Dual‐Atom Catalysts for Photocatalytic CO2 Reduction to CO and HCOOH: A Machine Learning and DFT Combined Approach

YBYingmei BianHainan University YWYanxin WangQingdao University ZYZijun YangHainan University

Key Points

The machine learning model predicts limiting potentials for CO and HCOOH with low average errors, enhancing catalyst selection.
Using a feedforward neural network, screening time was reduced by 56% for CO and 42% for HCOOH products.
High-throughput screening combined density functional theory and machine learning to evaluate dual-atom catalysts efficiently.
The findings may guide future experimental synthesis, underscoring the potential of these catalysts for energy conversion.

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.

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