exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914190490337280 |
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| author | Xia, Weiyi Moraru, Maxim Li, Ying Wai Wang, Cai-Zhuang |
| author_facet | Xia, Weiyi Moraru, Maxim Li, Ying Wai Wang, Cai-Zhuang |
| contents | We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottlenecks in large-scale structure prediction through task-based parallelization, adaptive load balancing, and optimized data management for CPU and GPU architectures. The framework automates the end-to-end workflow, from generating candidate structures to evaluating formation energies and updating phase diagrams. Its modular design allows users to easily replace or extend components with custom machine learning models, alternative initial structure templates, and future structure generators, enabling flexible integration with emerging AI approaches. We demonstrate strong scaling across high-performance computing platforms and highlight applications to Na-B-C, Ce-Co-B, and Fe-Co-Zr systems, establishing exa-AMD as a robust and exascale-ready tool for accelerating the discovery and design of functional materials. exa-AMD is publicly available on GitHub, with detailed documentation and reproducible test cases to support community engagement and collaborative research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials Xia, Weiyi Moraru, Maxim Li, Ying Wai Wang, Cai-Zhuang Materials Science Computational Physics We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottlenecks in large-scale structure prediction through task-based parallelization, adaptive load balancing, and optimized data management for CPU and GPU architectures. The framework automates the end-to-end workflow, from generating candidate structures to evaluating formation energies and updating phase diagrams. Its modular design allows users to easily replace or extend components with custom machine learning models, alternative initial structure templates, and future structure generators, enabling flexible integration with emerging AI approaches. We demonstrate strong scaling across high-performance computing platforms and highlight applications to Na-B-C, Ce-Co-B, and Fe-Co-Zr systems, establishing exa-AMD as a robust and exascale-ready tool for accelerating the discovery and design of functional materials. exa-AMD is publicly available on GitHub, with detailed documentation and reproducible test cases to support community engagement and collaborative research. |
| title | exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2510.01170 |