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The recent advent of tailorable photonic materials is currently driving the development of durable, compact, chip-compatible devices for information- and quantum technologies, sustainable energy, harsh-environment sensing, aerospace, chemical and oil & gas industries. In this talk, we will discuss advanced machine-learning-assisted photonic designs, materials optimization, and fabrication approaches for the development of efficient thermophotovoltaic (TPV) systems, lightsail spacecrafts, high-T sensors utilizing TMN metasurfaces and beyond. We also explore the potential of TMNs (titanium nitride, zirconium nitride) and TCOs for switchable photonics, high-harmonic-based XUV generation, refractory metasurfaces for energy conversion, high-power applications, photodynamic therapy and photocatalysis. The emphasis will be put on novel machine-learning-driven design frameworks that leverage the emerging quantum solvers for meta-device optimization and bridge the areas of materials engineering, photonic design, and quantum technologies.
Alexandra Boltasseva (2024) studied this question.