16 papers
First-Principles Study of Magnetism, Electronic Structure, and Bonding in Nb-Mn-Ge Kagome Compounds
Wei-Shen Tee, Shiya Chen, Weiyi Xia +3
In this work, we systematically investigate the magnetic ground states, electronic structures, and bonding characteristics of the computationally predicted stable NbMn6Ge6, NbMn6Ge…
Emergent Noncollinearity and Near-Degenerate Magnetic Superlattices in AT6X6 Kagome Metals
Weiyi Xia, Wei-Shen Tee, Peter Minch +3
Ferromagnetic AT6X6 Kagome compounds are a popular class of systems in which quantum magnetism with topological features has been observed. These systems allow easy chemical substi…
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…
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…
Developing a Neural Network Machine Learning Interatomic Potential for Molecular Dynamics Simulations of La-Si-P Systems
Ling Tang, Weiyi Xia, Gayatri Viswanathan +3
While molecular dynamics (MD) is a very useful computational method for atomistic simulations, modeling the interatomic interactions for reliable MD simulations of real materials h…
Search for stable and low-energy Ce-Co-Cu ternary compounds using machine learning
Weiyi Xia, Wei-Shen Tee, Paul Canfield +2
Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of…
Giant magnetic anisotropy of Pb atoms in 3d-based magnets
Weiyi Xia, Cai-Zhuang Wang, Vladimir Antropov
Electronic structure analysis is performed to study the properties of several Pb-containing 3d-intermetallics. Our study reveals that binary metastable Co3Pb and Fe3Pb intermetalli…
Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs
Weiyi Xia, Wei-Shen Tee, Paul C. Canfield +6
The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds grea…
LaCoX (X = Pb, Bi, Sb): a demonstration of antagonistic pairs as a route to quasi-low dimensional ternary compounds
Tyler J. Slade, Nao Furukawa, Matthew Dygert +6
We outline how pairs of strongly immiscible elements, referred to here as antagonistic pairs, can be used to synthesize ternary compounds with quasi-reduced dimensional motifs. By…
Coexistence of low and high spin states in LaCoPb
Weiyi Xia, Vladimir Antropov, Yongxin Yao +1
The electronic structure and magnetic properties of a newly predicted stable ternary compound LaCoPb are studied using electronic structure analysis. The ground…