发表机构
Instituto de Química Aplicada del Litoral IQAL (UNL-CONICET); Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional sinc(i) (UNL-CONICET); General Chemistry (ALGC) - Materials Modelling Group, Vrije Universiteit Brussel (VUB)（应用化学海岸研究所（UNL-CONICET）; 信号、系统和计算智能研究所（UNL-CONICET）; 化学与材料建模组，布鲁塞尔自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于碳基结构的单原子催化剂设计难题，利用图神经网络框架预测过渡金属在石墨烯量子点上的吸附能，经密度泛函理论数据训练，模型效果良好且大幅降低计算成本，为新型催化剂设计提供有效工具。
AI 中文摘要
近年来，基于碳基结构的单原子催化剂因高催化活性和金属原子高效利用受到广泛关注。然而，通过第一性原理计算来设计和表征这些材料计算成本高昂，限制了大量可能构型的探索。本文开发了基于图神经网络（GNNs）的框架来预测过渡金属在石墨烯量子点（GQDs）上的吸附能。该模型用密度泛函理论计算数据训练，$R^2$达0.906，平均绝对误差为0.101 eV，计算成本比DFT降低约六个数量级。此方法为基于碳纳米结构的新型催化剂加速筛选和合理设计提供了有效工具。
英文摘要
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.
