Deep-learning framework has significantly impelled the development of modern machine learning technology by continuously pushing the limit of traditional recognition and processing of images, speech, and videos. In the meantime, it starts to penetrate other disciplines, such as biology, genetics, materials science, and physics. Here, we report a deep-learning-based model, comprising two bidirectional neural networks assembled by a partial stacking strategy, to automatically design and optimize three-dimensional chiral metamaterials with strong chiroptical responses at predesignated wavelengths. The model can help to discover the intricate, nonintuitive relationship between a metamaterial structure and its optical responses from a number of training examples, which circumvents the time-consuming, case-by-case numerical simulations in conventional metamaterial designs. This approach not only realizes the forward prediction of optical performance much more accurately and efficiently but also enables one to inversely retrieve designs from given requirements. Our results demonstrate that such a data-driven model can be applied as a very powerful tool in studying complicated light-matter interactions and accelerating the on-demand design of nanophotonic devices, systems, and architectures for real world applications.

mostly relies on physics-inspired methods, resorting to human knowledge such as physical insights revealed by simplified analytical modeling, similar experience transferred from previous practice, and intuition obtained by scientific reasoning. For example, many meta-atoms inherited traditional antenna designs with geometries like rectangle, [4] cross, [5] bowtie, [6] V-shape, [7] H-shape, [8] and so on, whose first-order response is approximated by electrical dipole resonance with relevant scaling effect. [9] Some other designs guided by physical intuition include ring-like structures that exhibit strong magnetic resonances induced by the incident magnetic field, [10][11][12] dielectric building blocks that can induce both electric and magnetic resonances leading to better control of the phase of the scattered light, [13][14][15] or the spectra line-shape tailoring by introducing coupling among different resonant modes. [16][17][18] Despite the exciting results obtained by these physics-inspired designs, this methodology basically relies on a trial-and-error process, usually involving numerical methods like finite-difference-time-domain (FDTD) or finite element method (FEM) to iteratively solve Maxwell's equations. The low efficiency and thus limited exploration of the design varieties tend to easily omit the optimal solution. The inverse design approaches start from the opposite end, and try to optimize certain objective functions describing the desired performance. [19,20] Common approaches for inverse problems include genetic algorithm, [21] level set methods, [22] and topology optimization, [23] which, however, are still stochastic searching algorithms that are time-consuming and deteriorate rapidly as the design space grows. Different from numerical calculations, data-driven methods based on machine learning (ML) solve the optimization problem from statistical perspectives, so that the solution to optimize a target can be approximately generalized from numerous design examples. With the rapidly accumulated data and thus booming of deep learning (DL), the state-of-theart in many research domains, such as speech recognition, [24] computer vision, [25,26] natural language processing, [27] and decision making, [28] has been pushed far beyond conventional methods. Deep neural networks simulate biological signal processing that allow computational models to learn multiple The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed ...

Optical chiral metamaterials have recently attracted considerable attention because they offer new and exciting opportunities for fundamental research and practical applications. Through pragmatic designs, the chiroptical response of chiral metamaterials can be several orders of magnitude higher than that of natural chiral materials. Meanwhile, the local chiral fields can be enhanced by plasmonic resonances to drive a wide range of physical and chemical processes in both linear and nonlinear regimes. In this review, we will discuss the fundamental principles of chiral metamaterials, various optical chiral metamaterials realized by different nanofabrication approaches, and the applications and future prospects of this emerging field.

Hydrogenation of CO2 to methanol utilizing the hydrogen from renewable energy sources offers a promising way to reduce CO2 emissions through the CO2 utilization as a carbon source. However, it is a challenge to convert CO2 to methanol with high activity and high methanol selectivity. Herein, we report a class of metal-oxide solid-solution catalysts: MaZrO x (Ma = Cd, Ga), which show a methanol selectivity up to 80% with the CO2 single pass conversion reaching 4.3%–12.4% under the reaction conditions of H2/CO2 = 3/1, 24 000 h–1, 5 MPa. Structural and electronic characterizations combined with denisty functional theory calculations suggest that the Ma and Zr components in MaZrO x (Ma = Cd, Ga) solid-solution catalysts show a strong synergetic effect, which enhances the H2 heterolytic dissociation and results in high activity and high methanol selectivity. The solid-solution catalyst with dual metal oxide components offers an approach for the selective hydrogenation of CO2 to chemicals.

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