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This research applies graph neural networks to drastically accelerate the screening of single-atom catalysts on graphene quantum dots, replacing computationally expensive density functional theory calculations with a model that maintains high accuracy. By reducing computational costs by six orders of magnitude, this framework enables rapid exploration of material configurations previously infeasible for standard first-principles methods. The work represents a significant advancement in using machine learning to solve complex problems at the intersection of materials science and quantum systems.
GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots
arXiv:2607.18591v1 Announce Type: cross 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.
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.
Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2607.18591 [cond-mat.mtrl-sci]
(or arXiv:2607.18591v1 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2607.18591 arXiv-issued DOI via DataCite (pending registration)
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