Accelerated Design of Dual-Metal-Site Catalysts via Machine-Learning Prediction.

Abstract

Dual-metal site catalysts (DMSCs) supported on nitrogen-doped graphene have shown great potential in heterogeneous catalysis due to their unique properties and enhanced efficiency. However, the precise control and stabilization of metal dimers, particularly in oxygen activation reactions, present significant challenges in practical applications. In this study, we integrate high-throughput density functional theory calculations with machine learning techniques to predict and optimize the catalytic properties of DMSCs. Transfer learning is employed to enhance the model's generalization capability, successfully predicting catalytic performance across new metal combinations. Additionally, the application of the SISSO method enables the derivation of interpretable symbolic regression models, revealing critical correlations between electronic structure features and catalytic efficiency. This approach not only advances the understanding of dual-metal site catalysis but also provides a novel framework for the systematic design and optimization of highly efficient catalysts, with broad applicability in catalytic science.