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With the rapid development of data-driven machine learning (ML) models in meteorology, typhoon track forecasts have become increasingly accurate. However, current ML models still face challenges, such as underestimating typhoon intensity and lacking interpretability. To address these issues, this study establishes an ML-driven hybrid typhoon model, where forecast fields from the Pangu-Weather model are used to constrain the large-scale forecasts of the Weather Research and Forecasting model based on the spectral nudging method (Pangu_SP). The results show that forecasts from the Pangu_SP experiment obviously outperform those by using the Global Forecast System as the initial field (GFS_INIT) and from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF IFS) for the track forecast of Typhoon Doksuri (2023). The predicted typhoon cloud patterns from Pangu_SP are also more consistent with satellite observations. Additionally, the typhoon intensity forecasts from Pangu_SP are notably more accurate than those from the ECMWF IFS, demonstrating that the hybrid model effectively leverages the strengths of both ML and physical models. Furthermore, this study is the first to explore the significance of data assimilation in ML-driven hybrid dynamical systems. The findings reveal that after assimilating water vapor channels from the Advanced Geostationary Radiation Imager onboard Fengyun-4B, the errors in typhoon intensity forecasts are reduced., Comment: 12 pages, 4 figures

The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general., Comment: 21 pages, 5 figures

The exploration of van der Waals (vdWs) magnetic materials has sparked great interest in spintronics. However, conventional methods often face challenges in characterizing the magnetic properties of small-sized vdWs materials, especially for antiferromagnets with extremely small magnetic moments. Here, we demonstrate the efficacy of dynamic cantilever magnetometry (DCM) in characterizing the magnetic properties of vdWs magnets, using an antiferromagnetic semiconductor CrPS4. We observe continuous spin axis rotation under a magnetic field, accurately modelled by considering the existance of marked magnetic anisotropies. Furthermore, the dominance of out-of-plane magnetic anisotropy in spin reorientation behavior at low temperatures transitions to the prevalence of in-plane anisotropy with increasing temperature, leading to a sign reversal of the frequency shift in measurements. The peculiar magnetic phase transitions make CrPS4 an intriguing platform for studying two-dimensional magnetism. Our findings underscore the effectiveness of DCM in characterizing magnetic anisotropies and phase transitions in vdWs magnets.

Metasurface-based nano-printing has enabled ultrahigh-resolution grayscale or color image display. However, the maximum number of independent nano-printing images allowed by one single-layer metasurface is still limited despite many multiplexing methods that have been proposed to increase the design degree of freedom. In this work, we substantially push the multiplexing limit of nano-printing by transforming images at different observation angles into mapping the corresponding images to different positions in the Fourier space, and simultaneously controlling the complex electric field across multiple polarization channels. Our proposed Polarization-Encoded Lenticular Nano-Printing (Pollen), aided by a modified evolutionary algorithm, allows the display of several images based on the viewing angle, similar to traditional lenticular printing but without requiring a lenticular layer. In addition, it extends the display capability to encompass multiple polarization states. Empowered by the ability to control the complex amplitude of three polarization channels, we numerically and experimentally demonstrate the generation of 13 distinguished gray-scale Chinese ink wash painting images, 49 binary patterns, and three sets of 3D nano-printing images, totaling 25 unique visuals. These results present the largest number of recorded images with ultra-high resolution to date. Our innovative Pollen technique is expected to benefit the development of modern optical applications, including but not limited to optical encryption, optical data storage, lightweight display, and augmented reality and virtual reality., Comment: 12 pages, 5 figures