Deep Learning Guided Twistronics for Self-assembled Quantum Optoelectronics
Atomically thin two-dimensional (2D) materials can host intriguing quantum properties not found in their bulk counterparts. Furthermore, stacking 2D materials with control over the twist angles between adjacent layers provides a versatile way to obtain novel quantum materials with unprecedented properties. Such “twistronic” materials can have applications in electronics, photonics and quantum information science and technologies. However, with the new degrees of freedom, the materials design parameter space becomes exceedingly large, posing a significant challenge to predictably design and precisely make materials to enable such unique properties. In this DMREF project, the collaborative team from University of Pennsylvania, University of Wisconsin-Madison, and Northeastern University will use computer aided deep learning models and theoretical tools to predict designer twistronic materials prepared in specific states and guide the unique self-assembled crystal growth to engineer twist angles in different 2D materials. The team will perform property measurements to characterize these systems and also extend the ideas to quantum photonics to assemble on-chip devices. Results from synthesis, characterization and device measurements will be fed back to the theoretical models for establishing a self-consistent and tightly integrated research for further discovery of new designer twistronic materials with precisely controlled responses that can enable a new paradigm for quantum materials research with applications in computing, communications, imaging and sensing. Interdisciplinary research activities will be integrated with educational and outreach initiatives by involving students at all levels from diverse backgrounds in the collaborative research project with emphasis on quantum materials and photonics.