Engineering Wave-Material Interactions: From Multi-Physics Modeling to AI-Aided Next-Generation Electronics

Abstract: As communication, sensing, computing and quantum technologies move beyond conventional electronics, new functionality increasingly comes from functional materials with strongly coupled electrical, magnetic, mechanical and quantum responses. Coupling electromagnetic waves with ferroic order, spin dynamics, strain and superconducting states can produce device behaviors not accessible in conventional platforms. My research focuses on this materials-to-device pathway: identifying useful electrical phenomena in complex materials, understanding how composition, interfaces and coupled excitations control response, and translating those insights into new device concepts.

In this talk, I will highlight how ferroic, spintronic and superconducting materials can enable miniaturized antennas, low-power logic and hybrid quantum hardware, with examples including strain-mediated magnetoelectric antennas, magnetoelectric spin-orbit logic and negative-capacitance transistors. I will also discuss how artificial intelligence (AI) is reshaping this workflow: both as a fast surrogate for complex spatiotemporal dynamics and as a multimodal tool (such as multimodal language learning models) for linking simulation, measurement and materials data to accelerate interpretation and design. Working closely with experimental collaborators, we use this combined approach to close the loop between materials discovery, characterization and device realization. I will conclude with future directions in AI-guided co-design of functional materials and devices for next-generation technologies.