Adaptive multi-spectral mimicking with 2D-material nanoresonator networks

Abstract: Active nanophotonic materials that can emulate and adapt between many different spectral profiles -- with high fidelity and over a broad bandwidth -- could have a far-reaching impact, but are challenging to design due to a high-dimensional and complex design space. Here, we show that a metamaterial network of coupled 2D-material nanoresonators in graphene can adaptively match multiple complex absorption spectra via a set of input voltages. To design such networks, we develop a semi-analytical auto-differentiable dipole-coupled model that allows scalable optimization of high-dimensional networks with many elements and voltage signals. As a demonstration of multi-spectral capability, we design a single network capable of mimicking four spectral targets resembling select gases (nitric oxide, nitrogen dioxide, methane, nitrous oxide) with very high fidelity (${>}\,90\%$). Our results are relevant for the design of highly reconfigurable optical materials and platforms for applications in sensing, communication and display technology, and signature and thermal management.