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

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

Distributed, Parallel, and Cluster Computing 2025-06-27 v1

Authors: Maxim Moraru , Weiyi Xia , Zhuo Ye , Feng Zhang , Yongxin Yao , Ying Wai Li , Cai-Zhuang Wang

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.

Keywords

compiler optimization sequence alignment software library

Cite

@article{arxiv.2506.21449,
  title  = {exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design},
  author = {Maxim Moraru and Weiyi Xia and Zhuo Ye and Feng Zhang and Yongxin Yao and Ying Wai Li and Cai-Zhuang Wang},
  journal= {arXiv preprint arXiv:2506.21449},
  year   = {2025}
}

Comments

We intend to publish the paper to the Journal of Open Source Software

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

Abstract

Keywords

Cite

Comments

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