LASSP & AEP Seminar: Ting Cao (University of Washington)

Description

New Theoretical Insights into Moiré Solids and 2D Magnets from Machine Learning Assisted First-Principles Calculations

This talk will show our recent theoretical and computational investigations into the moire' superlattices and 2D magnets. We start by demonstrating that a deep neural network guided by first-principles data can be used to examine moire' structural reconstructions in various homobilayers and heterobilayers of transition metal dichalcogenides.

Going beyond the capacity of direct DFT calculations, our machine-learning  enabled workflow discovers salient structural features and key topological characters controlled by twist angles, layer composition, and other tuning knobs. This knowledge can be used to inform accurate continuum model, and to predict new forms of moire' potential and moire' topology.

In the second part, we show that the magnetism in 2D magnets like CrSBr is highly tunable by strain, pressure, chemical doping, and interfacing with other vdW materials like graphene. This allows for multi-demensional control of magnetic orders and noncollinear magnetism.  We demonstrate the strong light-matter interactions and unique excitonic structures in CrSBr provide possibility to control material transparency by ultrafast means.

Finally, we connect our theoretical discoveries to experimental results and explore potential applications.
  

Bio:
Ting Cao is an assistant professor of Materials Sciences & Enginerring at the University of Washington. His research uses quantum physics, machine learning, and high-performance parallel computing to understand condenensed matter and predict material properties.