8 papers
A Multi-Scale Machine Learning Framework for Coupled Chemical, Spin, and Structural Disorder in Alloys
Zhenyao Fang, Qimin Yan
Understanding the thermodynamic properties of disordered magnetic alloys requires a unified treatment of configurational (chemical, spin, etc.) and structural degrees of freedom, w…
Ideal Topological Flat Bands in Two-dimensional Moiré Heterostructures with Type-II Band Alignment
Yunzhe Liu, Anoj Aryal, Kaijie Yang +6
Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moiré materials. In this wo…
Roadmap: 2D Materials for Quantum Technologies
Qimin Yan, Tongcang Li, Xingyu Gao +29
Two-dimensional (2D) materials have emerged as a versatile and powerful platform for quantum technologies, offering atomic-scale control, strong quantum confinement, and seamless i…
Accurate Prediction of Tensorial Spectra Using Equivariant Graph Neural Network
Ting-Wei Hsu, Zhenyao Fang, Arun Bansil +1
Optical spectroscopies provide a powerful tool for harnessing light-matter interactions for unraveling complex electronic features such as the flat bands and nontrivial topologies…
Coherent Spins in van der Waals Semiconductor GeS2 at Ambient Conditions
Sumukh Vaidya, Xingyu Gao, Saakshi Dikshit +5
Optically active spin defects in van der Waals (vdW) materials have recently emerged as versatile quantum sensors, enabling applications from nanoscale magnetic field detection to…
A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials
Zhenyao Fang, Ting-Wei Hsu, Qimin Yan
Disorder, though naturally present in experimental samples and strongly influencing a wide range of material phenomena, remains underexplored in first-principles studies due to the…