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An artificial intrinsic RGB photoreceptor matrix can replicate the neuromorphic panchromatic vision capability of the eye. 

Abstract Halide perovskites show ubiquitous presences in growing fields at both fundamental and applied levels. Discovery, investigation, and application of innovative perovskites are heavily dependent on the synthetic methodology in terms of time-/yield-/effort-/energy- efficiency. Conventional wet chemistry method provides the easiness for growing thin film samples, but represents as an inefficient way for bulk crystal synthesis. To overcome these, here we report a universal solid state-based route for synthesizing high-quality perovskites, by means of simultaneously applying both electric and mechanical stress fields during the synthesis, i.e., the electrical and mechanical field-assisted sintering technique. We employ various perovskite compositions and arbitrary geometric designs for demonstration in this report, and establish such synthetic route with uniqueness of ultrahigh yield, fast processing and solvent-free nature, along with bulk products of exceptional quality approaching to single crystals. We exemplify the applications of the as-synthesized perovskites in photodetection and thermoelectric as well as other potentials to open extra chapters for future technical development. 

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to link weight prediction problem. This model extracts knowledge of nodes from known links’ weights and uses this knowledge to predict unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We anticipate this new approach to provide effective solutions to more graph mining tasks. 

Abstract Relaxor ferroelectrics (RFEs) are being actively investigated for energy‐storage applications due to their large electric‐field‐induced polarization with slim hysteresis and fast energy charging–discharging capability. Here, a novel nanograin engineering approach based upon high kinetic energy deposition is reported, for mechanically inducing the RFE behavior in a normal ferroelectric Pb(Zr_0.52Ti_0.48)O₃(PZT), which results in simultaneous enhancement in the dielectric breakdown strength (E_DBS) and polarization. Mechanically transformed relaxor thick films with 4 µm thickness exhibit an exceptionalE_DBSof 540 MV m⁻¹and reduced hysteresis with large unsaturated polarization (103.6 µC cm⁻²), resulting in a record high energy‐storage density of 124.1 J cm⁻³and a power density of 64.5 MW cm⁻³. This fundamental advancement is correlated with the generalized nanostructure design that comprises nanocrystalline phases embedded within the amorphous matrix. Microstructure‐tailored ferroelectric behavior overcomes the limitations imposed by traditional compositional design methods and provides a feasible pathway for realization of high‐performance energy‐storage materials.