Do Graph Neural Networks Work for High Entropy Alloys?
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.
Code (1)
Tasks
Property PredictionSimilar Papers 제목 키워드 기반
Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
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 ne…
Graph Neural NetworkGraph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys
Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The pres…
Graph Neural NetworkMachine learning-assisted design of high-entropy alloys for optimal strength and ductility
High Entropy Alloys (HEAs) are a novel class of multi-component alloys with compositional flexibility, presenting a promising alternative to traditional alloys. This research aims to enhance the strength of HEAs while ac…
ARCNeural network based order parameter for phase transitions and its applications in high-entropy alloys
Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order-disorder phase tra…
Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work…