用图神经网络预测高熵合金能量，兼顾局部原子与整体成分信息
Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
- 构建晶体图网络+分数嵌入网络融合模型，捕捉原子局部与全局特征
- 在1049个结构上训练，198个四元结构验证，均方根误差接近第一性原理
- 适合材料研发人员快速评估高熵合金能量，尤其对低能态结构有效
高熵合金因其复杂的原子构型展现出优异的力学与热学性能，备受关注。本文提出晶体分数图神经网络，通过显式融合局部原子环境与全局成分信息，实现高熵合金能量预测。模型包含三个部分：晶体图神经网络利用图注意力层学习晶格中16个近邻原子间的局部相互作用；分数神经网络为全连接网络，用于嵌入各组分元素的摩尔分数；特征融合网络将两子模型输出融合，预测总晶格能量。模型在1,049个晶体结构数据集上训练，并在198个四元结构上验证，所有超参数通过Optuna优化。结果表明，模型性能达到与第一性原理计算相当的均方根误差（RMSE），且在低能态结构上仍保持高精度。然而，模型在处理大晶胞时存在局限，未来工作将致力于拓展其对更复杂体系的适用性。
原文摘要 · Abstract (English)
High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we propose crystal fractional graph neural network for predicting the energy of high-entropy alloys by explicitly integrating both local atomic environments and global compositional information. The model consists of three components: a crystal graph neural network, which employs graph attention network layers to learn local interactions among 16 on-site atoms within the crystal lattice; fractional neural network, a fully connected network that embeds the global fraction of constituent elements; and feature fusion neural network, which fuses the outputs of the two submodels to predict the total crystal energy. We train the model on a dataset of 1,049 crystal structures and validate it on 198 quaternary structures, optimizing all hyperparameters via Optuna. Our results show that our model achieves an RMSE comparable to first-principles calculations and maintains high accuracy even for low-energy configurations. However, the model exhibits limitations in handling large crystal cells, which we aim to address in future work to extend its applicability to more complex systems.
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