GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots

2026-07-20

AI Summary

This work addresses the high computational cost of conventional first-principles methods in screening adsorption configurations of transition metal single-atom catalysts supported on graphene quantum dots. To overcome this limitation, we introduce graph neural networks (GNNs) into this domain for the first time, constructing a high-throughput predictive model trained on density functional theory (DFT) data to enable rapid and accurate prediction of adsorption energies. The resulting model achieves an R² of 0.906 and a mean absolute error of 0.101 eV on the test set, offering a computational speedup of approximately six orders of magnitude compared to DFT calculations. This dramatic acceleration significantly enhances the efficiency of rational catalyst design workflows.

Abstract

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.

Problem

• single-atom catalysts
• graphene quantum dots
• adsorption energy
• transition metals
• computational cost

Innovation

• Graph Neural Networks
• Graphene Quantum Dots
• Adsorption Energy Prediction
• Single-Atom Catalysts
• Accelerated Materials Screening