Materials Science Research Lecture

Webinar Link:

https://caltech.zoom.us/j/83276652110

Webinar ID: 832 7665 2110

NOTE: At this time, in-person APhMS seminars are open to all Caltech students/staff/faculty/visitors with a valid Caltech ID. Outside community members are welcome to join our online event.

Abstract:

The discovery of low-dimensional materials has opened opportunities in exploring new quantum properties as well as related applications which are absent in conventional bulk materials. This talk will show our recent theoretical and computational studies of new exciton physics in van der Waals semiconductors including monolayer transition metal dichalcogenides and 2D magnets. In the first part, we show that the photoelectrons from excitons hold unique energy dispersions and spectra weights, which unveil the fundamental physical properties of these excitons. First-principles calculations based on many-body perturbation theories unveil results that agree well with the measured pump-probe photoemission spectra in monolayer WSe2. We further demonstrate a valley- and spin-selective excitonic energy relaxation pathway, which leads to novel ultrafast dynamics and the discovery of selection rules for exciton-phonon couplings. In the second part, we present recent theoretical and first-principles studies of excitons in 2D magnetic semiconductors. We show that 2D magnetic semiconductors exhibit a rich set of excitons of Frenkel or Wannier types, which encode excited-state information dictated by the underlying crystal structure and magnetic order. We then present the roles of interlayer couplings on the excitons and optical properties of these materials. We further connect our theoretical discoveries to experimental results and explore their potential applications.

Bio:

Ting Cao is an assistant professor of Materials Science & Engineering at the University of Washington. His research uses quantum physics, advanced modelling techniques, and high-performance parallel computing to understand condensed matter and predict material properties.