Do Graph Neural Networks Work for High Entropy Alloys?

• URL: http://arxiv.org/abs/2408.16337v1
• Date: Thu, 29 Aug 2024 08:20:02 GMT
• Title: Do Graph Neural Networks Work for High Entropy Alloys?
• Authors: Hengrui Zhang, Ruishu Huang, Jie Chen, James M. Rondinelli, Wei Chen,
• Abstract summary: High-entropy alloys (HEAs) lack chemical long-range order, limiting the applicability of current graph representations. 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 ofquaternary HEAs.
• Score: 12.002942104379986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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