11 papers
Discovery of novel magnetic Y-Mn-B compounds via advanced machine learning guided framework
Weiyi Xia, Wei Shen Tee, Maxim Moraru +2
Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain…
Antiferromagnetic Phases in Zr-Fe-Ge Kagome Systems
Peter Minch, Shiya Chen, Weiyi Xia +4
A wide variety of chemical substitutions in ferromagnetic Kagome systems can lead to diverse magnetic phases with electronic structures suitable for topological or quantum material…
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…
Complex crystal structure prediction using ML-enhanced multi-minima iterative genetic algorithm
Ling Tang, Weiyi Xia, Tyler J. Slade +2
Current machine learning (ML) approaches for materials discovery rely heavily on known structural databases, limiting their ability to identify entirely novel structure types. In t…
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…