Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment (LE) graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations.

翻译：图神经网络（GNNs）凭借图表示的强大表达能力，在晶体和分子的预测建模中表现出色。然而，高熵合金（HEAs）缺乏化学长程有序性，这限制了现有图表示的适用性。为克服这一挑战，我们提出将HEAs表示为局部环境（LE）图的集合。基于此表示，我们引入了LESets机器学习模型，这是一种用于HEA性质预测的精确且可解释的GNN。我们展示了LESets在建模四元HEAs力学性质方面的准确性。通过分析和解释，我们进一步提取了关于HEAs建模与设计的见解。从更广泛的意义上讲，LESets将GNNs的潜在适用性扩展到了由多样组成成分及其灵活构型形成的、具有组合复杂性的无序材料。