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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16336 |
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| _version_ | 1866912682483908608 |
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| author | Zhang, S. -Y. Tian, J. Liu, S. -L. Zhang, H. -M. Bai, H. -Y. Hu, Y. -C. Wang, W. -H. |
| author_facet | Zhang, S. -Y. Tian, J. Liu, S. -L. Zhang, H. -M. Bai, H. -Y. Hu, Y. -C. Wang, W. -H. |
| contents | Designing high-performance amorphous alloys is demanding for various applications. But this process intensively relies on empirical laws and unlimited attempts. The high-cost and low-efficiency nature of the traditional strategies prevents effective sampling in the enormous material space. Here, we propose material networks to accelerate the discovery of binary and ternary amorphous alloys. The network topologies reveal hidden material candidates that were obscured by traditional tabular data representations. By scrutinizing the amorphous alloys synthesized in different years, we construct dynamical material networks to track the history of the alloy discovery. We find that some innovative materials designed in the past were encoded in the networks, demonstrating their predictive power in guiding new alloy design. These material networks show physical similarities with several real-world networks in our daily lives. Our findings pave a new way for intelligent materials design, especially for complex alloys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constructing material network representations for intelligent amorphous alloys design Zhang, S. -Y. Tian, J. Liu, S. -L. Zhang, H. -M. Bai, H. -Y. Hu, Y. -C. Wang, W. -H. Materials Science Disordered Systems and Neural Networks Computational Complexity Machine Learning Designing high-performance amorphous alloys is demanding for various applications. But this process intensively relies on empirical laws and unlimited attempts. The high-cost and low-efficiency nature of the traditional strategies prevents effective sampling in the enormous material space. Here, we propose material networks to accelerate the discovery of binary and ternary amorphous alloys. The network topologies reveal hidden material candidates that were obscured by traditional tabular data representations. By scrutinizing the amorphous alloys synthesized in different years, we construct dynamical material networks to track the history of the alloy discovery. We find that some innovative materials designed in the past were encoded in the networks, demonstrating their predictive power in guiding new alloy design. These material networks show physical similarities with several real-world networks in our daily lives. Our findings pave a new way for intelligent materials design, especially for complex alloys. |
| title | Constructing material network representations for intelligent amorphous alloys design |
| topic | Materials Science Disordered Systems and Neural Networks Computational Complexity Machine Learning |
| url | https://arxiv.org/abs/2507.16336 |