exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866915360127582208 |
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| author | Moraru, Maxim Xia, Weiyi Ye, Zhuo Zhang, Feng Yao, Yongxin Li, Ying Wai Wang, Cai-Zhuang |
| author_facet | Moraru, Maxim Xia, Weiyi Ye, Zhuo Zhang, Feng Yao, Yongxin Li, Ying Wai Wang, Cai-Zhuang |
| contents | exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from execution configuration, thereby empowering researchers to scale their workflows without having to reimplement them for each system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21449 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design Moraru, Maxim Xia, Weiyi Ye, Zhuo Zhang, Feng Yao, Yongxin Li, Ying Wai Wang, Cai-Zhuang Distributed, Parallel, and Cluster Computing exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows. The execution model of exa-AMD relies on Parsl, a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers. By using Parsl, exa-AMD is able to decouple the workflow logic from execution configuration, thereby empowering researchers to scale their workflows without having to reimplement them for each system. |
| title | exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.21449 |