标题： EOSnet：用于预测材料性质的图神经网络中的嵌入重叠结构 显示英文标题

标题： EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties

摘要： 图神经网络（GNNs）已成为预测材料性质的强大工具，但它们往往难以捕捉多体相互作用，并且需要大量的手动特征工程。 在此，我们提出EOSnet（用于图神经网络的嵌入重叠结构），这是一种新方法，通过在GNN架构中将高斯重叠矩阵（GOM）指纹作为节点特征，解决了这些限制。 与依赖显式角度项或人工设计特征的模型不同，EOSnet通过轨道重叠矩阵高效地编码多体相互作用，提供了原子环境的旋转不变且可转移的表示。 该模型在各种材料性质预测任务中表现出色，在对多体相互作用敏感的性质上取得了特别显著的结果。 对于带隙预测，EOSnet的平均绝对误差为0.163 eV，超过了之前最先进的模型。 该模型在预测机械性质和分类材料方面也表现出色，金属/非金属分类的准确率为97.7%。 这些结果表明，将GOM指纹嵌入节点特征可以增强GNN捕捉复杂原子相互作用的能力，使EOSnet成为材料发现和性质预测的强大工具。 显示英文摘要

摘要： Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments. The model demonstrates superior performance across various materials property prediction tasks, achieving particularly notable results in properties sensitive to many-body interactions. For band gap prediction, EOSnet achieves a mean absolute error of 0.163 eV, surpassing previous state-of-the-art models. The model also excels in predicting mechanical properties and classifying materials, with 97.7\% accuracy in metal/non-metal classification. These results demonstrate that embedding GOM fingerprints into node features enhances the ability of GNNs to capture complex atomic interactions, making EOSnet a powerful tool for materials discovery and property prediction.