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Multilayer films with continuously varying indices for each layer have attracted great deal of attention due to their superior optical, mechanical, and thermal properties. However, difficulties in fabrication have limited their application and study in scientific literature compared to multilayer films with fixed index layers. In this work we propose a neural network based inverse design technique enabled by a differentiable analytical solver for realistic design and fabrication of single material variable-index multilayer films. This approach generates multilayer films with excellent performance under ideal conditions. We furthermore address the issue of how to translate these ideal designs into practical useful devices which will naturally suffer from growth imperfections. By integrating simulated systematic and random errors just as a deposition tool would into the optimization process, we demonstrated that the same neural network that produced the ideal device can be retrained to produce designs compensating for systematic deposition errors. Furthermore, the proposed approach corrects for systematic errors even in the presence of random fabrication imperfections. The results outlined in this paper provide a practical and experimentally viable approach for the design of single material multilayer film stacks for an extremely wide variety of practical applications with high performance. 

Abstract Solar-thermal technologies for converting chemicals using thermochemistry require extreme light concentration. Exploiting plasmonic nanostructures can dramatically increase the reaction rates by providing more efficient solar-to-heat conversion by broadband light absorption. Moreover, hot-carrier and local field enhancement effects can alter the reaction pathways. Such discoveries have boosted the field of photothermal catalysis, which aims at driving industrially-relevant chemical reactions using solar illumination rather than conventional heat sources. Nevertheless, only large arrays of plasmonic nano-units on a substrate, i.e., plasmonic metasurfaces, allow a quasi-unitary and broadband solar light absorption within a limited thickness (hundreds of nanometers) for practical applications. Through moderate light concentration (∼10 Suns), metasurfaces reach the same temperatures as conventional thermochemical reactors, or plasmonic nanoparticle bed reactors reach under ∼100 Suns. Plasmonic metasurfaces, however, have been mostly neglected so far for applications in the field of photothermal catalysis. In this Perspective, we discuss the potentialities of plasmonic metasurfaces in this emerging area of research. We present numerical simulations and experimental case studies illustrating how broadband absorption can be achieved within a limited thickness of these nanostructured materials. The approach highlights the synergy among different enhancement effects related to the ordered array of plasmonic units and the efficient heat transfer promoting faster dynamics than thicker structures (such as powdered catalysts). We foresee that plasmonic metasurfaces can play an important role in developing modular-like structures for the conversion of chemical feedstock into fuels without requiring extreme light concentrations. Customized metasurface-based systems could lead to small-scale and low-cost decentralized reactors instead of large-scale, infrastructure-intensive power plants. 

The generation of nonequilibrium hot-carriers from the decay of surface plasmons has been attracting intense research attention in the last decade due to both the fundamental aspects of extreme light-matter interactions and potential practical applications. Here, we overview the physics associated with plasmon-assisted hot-carrier generation and outline the key applications of hot-carrier processes for photodetection, photovoltaics and photocatalysis. We also discuss the recent developments in employing molecular tunnel junctions as barriers for extracting hot-carriers and provide an outlook on the potential of this emerging field for sustainable energy. 

In the recent years, there is a growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework’s performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications. 

Abstract Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in-plane geometry, out-of-plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning–assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics-driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design “intuition” by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions.