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arXiv · 2506.21449

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design

Maxim Moraru·Weiyi Xia·Zhuo Ye·Feng Zhang·Yongxin Yao·Ying Wai Li·Cai-Zhuang Wang
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Abstract

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.

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Maxim Moraru, Weiyi Xia, Zhuo Ye, Feng Zhang, Yongxin Yao, Ying Wai Li, Cai-Zhuang Wang. 2025-06-26. exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design. https://arxiv.org/abs/2506.21449

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