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Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

ABSTRACT AimGlobal climate change is compressing species' realised niches and further threatening their distributions. Species traits, especially the trait spectra synthesised from traits, are one way in which species can match changes in their environment. Hence, integrating trait spectra and niches will help us understand how species adapt to their environment under global change. LocationGlobal. Time PeriodPresent. Major Taxa StudiedAngiosperms. MethodWe collected root traits from 158 angiosperm species and leaf traits from 512 angiosperm species from a global trait database to construct the leaf and root trait ‘slow‐fast’ spectrum based on resource acquisition strategy, as well as the collaboration spectrum related to root mycorrhizal colonisation. After rebuilding their phylogenetic relationships and defining species' environmental niches based on 213,979 occurrences of these species, we examined the relationship between these trait spectra and environmental niches along global climatic patterns. ResultPlants with ‘slow’ leaf traits were generally associated with narrow niche breadths and marginal niche positions, especially in high precipitation areas. The relationship between the ‘slow‐fast’ spectrum in root traits and ‘marginal‐central’ niche position reversed with decreasing precipitation. However, the relationships between leaf traits and niche variables were significant for woody species but not for herbaceous species. Main ConclusionOur research expands the plant trait spectra in macroecology applications. The root and leaf ‘slow‐fast’ trait spectra of angiosperms are driven by both macroclimate and long‐term evolutionary pressure. Understanding how these traits relate to the niche of species helps to predict how that species is likely to adapt to environmental change, which can enhance the predictive ability of niche theory for plant environmental adaptability. 

Metasurfaces have recently emerged as a promising technological platform, offering unprecedented control over light by structuring materials at the nanoscale using two-dimensional arrays of subwavelength nanoresonators. These metasurfaces possess exceptional optical properties, enabling a wide variety of applications in imaging, sensing, telecommunication, and energy-related fields. One significant advantage of metasurfaces lies in their ability to manipulate the optical spectrum by precisely engineering the geometry and material composition of the nanoresonators’ array. Consequently, they hold tremendous potential for efficient solar light harvesting and conversion. In this Review, we delve into the current state-of-the-art in solar energy conversion devices based on metasurfaces. First, we provide an overview of the fundamental processes involved in solar energy conversion, alongside an introduction to the primary classes of metasurfaces, namely, plasmonic and dielectric metasurfaces. Subsequently, we explore the numerical tools used that guide the design of metasurfaces, focusing particularly on inverse design methods that facilitate an optimized optical response. To showcase the practical applications of metasurfaces, we present selected examples across various domains such as photovoltaics, photoelectrochemistry, photocatalysis, solar-thermal and photothermal routes, and radiative cooling. These examples highlight the ways in which metasurfaces can be leveraged to harness solar energy effectively. By tailoring the optical properties of metasurfaces, significant advancements can be expected in solar energy harvesting technologies, offering new practical solutions to support an emerging sustainable society.