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

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design - Published in JOSS (2025)

https://github.com/ml-amd/exa-amd

Science Score: 87.0%

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    Published in Journal of Open Source Software
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JOSS Publication

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
Published
November 17, 2025
Volume 10, Issue 115, Page 8879
Authors
Maxim Moraru
Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America
Weiyi Xia
Ames Laboratory, US DOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, United States of America
Zhuo Ye
Ames Laboratory, US DOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, United States of America
Feng Zhang
Ames Laboratory, US DOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, United States of America
Yongxin Yao
Ames Laboratory, US DOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, United States of America
Ying Wai Li
Los Alamos National Laboratory, Los Alamos, NM 87545, United States of America
Cai-Zhuang Wang
Ames Laboratory, US DOE and Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, United States of America
Editor
Evan Spotte-Smith ORCID
Tags
Machine learning Material databases Heterogeneity HPC workflows