5 papers
exa-PD: A scalable high-performance workflow for multi-element phase diagram construction
Zhuo Ye, Feng Zhang, Maxim Moraru +4
Exa-PD is a highly parallelizable workflow designed for the construction of multi-element phase diagrams (PDs). It uses standard sampling techniques, molecular dynamics (MD) and Mo…
exaPD: A highly parallelizable workflow for multi-element phase diagram (PD) construction
Feng Zhang, Zhuo Ye, Maxim Moraru +4
Phase diagrams (PDs) illustrate the relative stability of competing phases under varying conditions, serving as critical tools for synthesizing complex materials. Reliable phase di…
exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials
Weiyi Xia, Maxim Moraru, Ying Wai Li +1
We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottleneck…
Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing
Weiyi Xia, Maxim Moraru, Ying Wai Li +3
Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable…
exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design
Maxim Moraru, Weiyi Xia, Zhuo Ye +4
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 mechanica…