6 papers · 1 filter
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…
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…
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…