8 citations · 14 across the 10 of their papers we have counts for
11 papers
Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design
Ting-Wei Hsu, Arun Bansil, Qimin Yan
The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a favorable numerical value is mea…
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…
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…
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 wor…
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…
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…
Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method
Zhenyao Fang, Ting-Wei Hsu, Qimin Yan
The discovery and design of functional materials for energy harvesting and electronic applications require accurate predictions of their optical and transport properties. While sev…
Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks
Zhenyao Fang, Qimin Yan
In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial to the accurate prediction of defect propert…
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
Alex Heilman, Claire Schlesinger, Qimin Yan
Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, a…
Towards Accurate Prediction of Configurational Disorder Properties in Materials using Graph Neural Networks
Zhenyao Fang, Qimin Yan
The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient an…