Do Graph Neural Networks Work for High Entropy Alloys?
This work addresses property prediction for materials science researchers studying disordered materials like HEAs, representing an incremental advancement by adapting GNNs to a new domain-specific challenge.
The paper tackled the problem of predicting properties for high-entropy alloys (HEAs), which lack chemical long-range order, by proposing a graph neural network (GNN) model called LESets that represents HEAs as local environment graphs. The result demonstrated accurate modeling of mechanical properties for quaternary HEAs, though no concrete numbers were provided in the abstract.
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.