Integrating conventional optics into compact nanostructured surfaces is the goal of flat optics. Despite the enormous progress in this technology, there are still critical challenges for real-world applications due to the limited operational efficiency in the visible region, on average lower than 60%, which originates from absorption losses in wavelength-thick (≈ 500 nm) structures. Another issue is the realization of on-demand optical components for controlling vectorial light at visible frequencies simultaneously in both reflection and transmission and with a predetermined wavefront shape. In this work, we developed an inverse design approach that allows the realization of highly efficient (up to 99%) ultrathin (down to 50 nm thick) optics for vectorial light control with broadband input–output responses in the visible and near-IR regions with a desired wavefront shape. The approach leverages suitably engineered semiconductor nanostructures, which behave as a neural network that can approximate a user-defined input–output function. Near-unity performance results from the ultrathin nature of these surfaces, which reduces absorption losses to near-negligible values. Experimentally, we discuss polarizing beam splitters, comparing their performance with the best results obtained from both direct and inverse design techniques, and new flat-optics components represented by dichroic mirrors and the basic unit of a flat-optics display that creates full colours by using only two subpixels, overcoming the limitations of conventional LCD/OLED technologies that require three subpixels for each composite colour. Our devices can be manufactured with a complementary metal-oxide-semiconductor (CMOS)-compatible process, making them scalable for mass production at low cost.

Electrocatalytic two‐electron oxygen reduction (2e− ORR) to hydrogen peroxide (H2O2) is attracting broad interest in diversified areas including paper manufacturing, wastewater treatment, production of liquid fuels, and public sanitation. Current efforts focus on researching low‐cost, large‐scale, and sustainable electrocatalysts with high activity and selectivity. Here a large‐scale H2O2 electrocatalysts based on metal‐free carbon fibers with a fluorine and sulfur dual‐doping strategy is engineered. Optimized samples yield with a high onset potential of 0.814 V versus reversible hydrogen electrode (RHE), an almost ideal 2e− pathway selectivity of 99.1%, outperforming most of the recently reported carbon‐based or metal‐based electrocatalysts. First principle theoretical computations and experiments demonstrate that the intermolecular charge transfer coupled with electron spin redistribution from fluorine and sulfur dual‐doping is the crucial factor contributing to the enhanced performances in 2e− ORR. This work opens the door to the design and implementation of scalable, earth‐abundant, highly selective electrocatalysts for H2O2 production and other catalytic fields of industrial interest.

In the past 20 years, flat‐optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat‐optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat‐optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large‐scale optimizers, equipped with fast‐trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat‐optics robust to both fabrication errors or user‐defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat‐optics for biosensing, imaging, and biomedical devices.

Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.