Do Graph Neural Networks Work for High Entropy Alloys?

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.

References in corpus (9)

• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Exceptional fracture toughness of CrCoNi-based medium- and high-entropy alloys close to liquid helium temperatures
• Equivariant message passing for the prediction of tensorial properties and molecular spectra
• On the redundancy in large material datasets: efficient and robust learning with less data
• Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
• Spin-Dependent Graph Neural Network Potential for Magnetic Materials
• MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling