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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 ( $>90\mathrm{\%}$). Our results could impact the design of highly reconfigurable optical materials and platforms for applications in sensing, communication and display technology, and signature and thermal management. 

Abstract Deep learning techniques have been increasingly applied to the natural sciences, e.g., for property prediction and optimization or material discovery. A fundamental ingredient of such approaches is the vast quantity of labeled data needed to train the model. This poses severe challenges in data-scarce settings where obtaining labels requires substantial computational or labor resources. Noting that problems in natural sciences often benefit from easily obtainable auxiliary information sources, we introduce surrogate- and invariance-boosted contrastive learning (SIB-CL), a deep learning framework which incorporates three inexpensive and easily obtainable auxiliary information sources to overcome data scarcity. Specifically, these are: abundant unlabeled data, prior knowledge of symmetries or invariances, and surrogate data obtained at near-zero cost. We demonstrate SIB-CL’s effectiveness and generality on various scientific problems, e.g., predicting the density-of-states of 2D photonic crystals and solving the 3D time-independent Schrödinger equation. SIB-CL consistently results in orders of magnitude reduction in the number of labels needed to achieve the same network accuracies. 

Bioremediation of chlorinated aliphatic hydrocarbon-contaminated aquifers can be hindered by high contaminant concentrations and acids generated during remediation. Encapsulating microbes in hydrogels may provide a protective, tunable environment from inhibiting compounds; however, current approaches to formulate successful encapsulated systems rely on trial and error rather than engineering approaches because fundamental information on mass-transfer coefficients is lacking. To address this knowledge gap, hydronium ion mass-transfer rates through two commonly used hydrogel materials, poly(vinyl alcohol) and alginic acid, under two solidification methods (chemical and cryogenic) were measured. Variations in hydrogel crosslinking conditions, polymer composition, and solvent ionic strength were investigated to understand how each influenced hydronium ion diffusivity. A three-way ANOVA indicated that the ionic strength, membrane type, and crosslinking method significantly (p < 0.001) contributed to changes in hydronium ion mass transfer. Hydronium ion diffusion increased with ionic strength, counter to what is observed in aqueous-only (no polymer) solutions. Co-occurring mechanisms correlated to increased hydronium ion diffusion with ionic strength included an increased water fraction within hydrogel matrices and hydrogel contraction. Measured diffusion rates determined in this study provide first principal design information to further optimize encapsulating hydrogels for bioremediation. 

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally adopted in optical material modeling. The growing interest in plasmonic, polaritonic, and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces—can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In metasurfaces, in particular, nonlocality engineering has emerged as a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials and metamaterials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward—toward new scientific discoveries and technological advancements.