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This study introduces a framework leveraging graph neural networks (GNNs) to predict the adsorption energies of transition metals on graphene quantum dots (GQDs), addressing the computational challenges posed by first-principles calculations. By training the model on data derived from density functional theory (DFT), the researchers achieved an impressive $R^2$ of 0.906 and a mean absolute error (MAE) of 0.101 eV, significantly enhancing predictive accuracy. The developed approach reduces computational costs by approximately six orders of magnitude compared to traditional DFT methods, facilitating rapid exploration of potential catalyst configurations.
Achieving a six-order reduction in computational cost while maintaining high predictive accuracy could revolutionize the design of transition metal catalysts on carbon nanostructures.
In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms. However, the design and characterization of these materials through first-principles calculations are computationally expensive, limiting the exploration of a large number of possible configurations. Here, we developed a framework based on graph neural networks (GNNs) to predict the adsorption energies of transition metals on graphene quantum dots (GQDs). The model was trained using data obtained from density functional theory calculations and achieved an $R^2$ of 0.906 with an MAE of 0.101 eV, while reducing computational cost by roughly six orders of magnitude relative to DFT. This methodology provides an efficient tool for the accelerated screening and rational design of new catalysts based on carbon nanostructures.