Do Graph Neural Networks Work for High Entropy Alloys?
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866908297751166976 |
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| author | Zhang, Hengrui Huang, Ruishu Chen, Jie Rondinelli, James M. Chen, Wei |
| author_facet | Zhang, Hengrui Huang, Ruishu Chen, Jie Rondinelli, James M. Chen, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16337 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Do Graph Neural Networks Work for High Entropy Alloys? Zhang, Hengrui Huang, Ruishu Chen, Jie Rondinelli, James M. Chen, Wei Machine Learning Materials Science 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. |
| title | Do Graph Neural Networks Work for High Entropy Alloys? |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2408.16337 |