用图神经网络快速预测金属在石墨烯量子点上的吸附能。
GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots
- 用图神经网络建模金属吸附行为，输入为原子结构图。
- 预测误差仅0.101 eV，R²达0.906，速度比DFT快百万倍。
- 适合材料筛选与催化剂设计，尤其适用于碳纳米结构。
近年来，单原子催化剂负载于碳基材料因其高催化活性和金属原子高效利用而备受关注。然而，通过第一性原理计算设计和表征这些材料计算成本高昂，限制了大量构型的探索。本文开发了一种基于图神经网络（GNN）的框架，用于预测过渡金属在石墨烯量子点（GQDs）上的吸附能。模型基于密度泛函理论（DFT）数据训练，达到R² = 0.906，平均绝对误差（MAE）为0.101 eV，同时相较DFT将计算成本降低约六数量级。该方法为基于碳纳米结构的新型催化剂加速筛选与理性设计提供了高效工具。
原文摘要 · Abstract (English)
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.
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