Room-Temperature Structures of Solid Hydrogen at High Pressures

By employing first-principles metadynamics simulations, we explore the 300 K structures of solid hydrogen over the pressure range 150-300 GPa. At 200 GPa, we find the ambient-pressure disordered hexagonal close-packed (hcp) phase transited into an insulating partially ordered hcp phase (po-hcp), a mixture of ordered graphene-like H2 layers and the other layers of weakly coupled, disordered H2 molecules. Within this phase, hydrogen remains in paired states with creation of shorter intra-molecular bonds, which are responsible for the very high experimental Raman peak above 4000 cm-1. At 275 GPa, our simulations predicted a transformation from po-hcp into the ordered molecular metallic Cmca phase (4 molecules/cell) that was previously proposed to be stable only above 400 GPa. Gibbs free energy calculations at 300 K confirmed the energetic stabilities of the po-hcp and metallic Cmca phases over all known structures at 220-242 GPa and >242 GPa, respectively. Our simulations highlighted the major role played by temperature in tuning the phase stabilities and provided theoretical support for claimed metallization of solid hydrogen below 300 GPa at 300 K.Comment: Accepted in Journal of Chemical Physic

Atomically phase-matched second-harmonic generation in a 2D crystal.

Second-harmonic generation (SHG) has found extensive applications from hand-held laser pointers to spectroscopic and microscopic techniques. Recently, some cleavable van der Waals (vdW) crystals have shown SHG arising from a single atomic layer, where the SH light elucidated important information such as the grain boundaries and electronic structure in these ultra-thin materials. However, despite the inversion asymmetry of the single layer, the typical crystal stacking restores inversion symmetry for even numbers of layers leading to an oscillatory SH response, drastically reducing the applicability of vdW crystals such as molybdenum disulfide (MoS2). Here, we probe the SHG generated from the noncentrosymmetric 3R crystal phase of MoS2. We experimentally observed quadratic dependence of second-harmonic intensity on layer number as a result of atomically phase-matched nonlinear dipoles in layers of the 3R crystal that constructively interfere. By studying the layer evolution of the A and B excitonic transitions in 3R-MoS2 using SHG spectroscopy, we also found distinct electronic structure differences arising from the crystal structure and the dramatic effect of symmetry and layer stacking on the nonlinear properties of these atomic crystals. The constructive nature of the SHG in this 2D crystal provides a platform to reliably develop atomically flat and controllably thin nonlinear media

Multi-resolution spatio-temporal prediction with application to wind power generation

Wind energy is becoming an increasingly crucial component of a sustainable grid, but its inherent variability and limited predictability present challenges for grid operators. The energy sector needs novel forecasting techniques that can precisely predict the generation of renewable power and offer precise quantification of prediction uncertainty. This will facilitate well-informed decision-making by operators who wish to integrate renewable energy into the power grid. This paper presents a novel approach to wind speed prediction with uncertainty quantification using a multi-resolution spatio-temporal Gaussian process. By leveraging information from multiple sources of predictions with varying accuracies and uncertainties, the joint framework provides a more accurate and robust prediction of wind speed while measuring the uncertainty in these predictions. We assess the effectiveness of our proposed framework using real-world wind data obtained from the Midwest region of the United States. Our results demonstrate that the framework enables predictors with varying data resolutions to learn from each other, leading to an enhancement in overall predictive performance. The proposed framework shows a superior performance compared to other state-of-the-art methods. The goal of this research is to improve grid operation and management by aiding system operators and policymakers in making better-informed decisions related to energy demand management, energy storage system deployment, and energy supply scheduling. This results in potentially further integration of renewable energy sources into the existing power systems

Optical Selection Rule based on Valley-Exciton Locking for 2D Valleytronics

Optical selection rule fundamentally determines the optical transition between energy states in a variety of physical systems from hydrogen atoms to bulk crystals such as GaAs. It is important for optoelectronic applications such as lasers, energy-dispersive X-ray spectroscopy and quantum computation. Recently, single layer transition metal dichalcogenide (TMDC) exhibits valleys in momentum space with nontrivial Berry curvature and excitons with large binding energy. However, it is unclear how the unique valley degree of freedom combined with the strong excitonic effect influences the optical excitation. Here we discover a new set of optical selection rules in monolayer WS2,imposed by valley and exciton angular momentum. We experimentally demonstrated such a principle for second harmonic generation (SHG) and two-photon luminescence (TPL). Moreover, the two-photon induced valley populations yield net circular polarized photoluminescence after a sub-ps interexciton relaxation (2p->1s) and last for 8 ps. The discovery of this new optical selection rule in valleytronic 2D system not only largely extend information degrees but sets a foundation in control of optical transitions that is crucial to valley optoeletronic device applications such as 2D valley-polarized light emitting diodes (LED), optical switches and coherent control for quantum computing

DP-Image: Differential Privacy for Image Data in Feature Space

The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even though differential privacy (DP) is a widely accepted criterion that can provide a provable privacy guarantee, the application of DP on unstructured data such as images is not trivial due to the lack of a clear qualification on the meaningful difference between any two images. In this paper, for the first time, we introduce a novel notion of image-aware differential privacy, referred to as DP-image, that can protect user's personal information in images, from both human and AI adversaries. The DP-Image definition is formulated as an extended version of traditional differential privacy, considering the distance measurements between feature space vectors of images. Then we propose a mechanism to achieve DP-Image by adding noise to an image feature vector. Finally, we conduct experiments with a case study on face image privacy. Our results show that the proposed DP-Image method provides excellent DP protection on images, with a controllable distortion to faces