Reversible Transition Between Thermodynamically Stable Phases with Low Density of Oxygen Vacancies on SrTiO3_3(110) Surface

The surface reconstruction of SrTiO$_3$(110) is studied with scanning tunneling microscopy and density functional theory (DFT) calculations. The reversible phase transition between (4$\times$1) and (5$\times$1) is controlled by adjusting the surface metal concentration [Sr] or [Ti]. Resolving the atomic structures of the surface, DFT calculations verify that the phase stability changes upon the chemical potential of Sr or Ti. Particularly, the density of oxygen vacancies is low on the thermodynamically stabilized SrTiO$_3$(110) surface.Comment: Accepted by Physical Review Letter

Coalescence of Carbon Atoms on Cu (111) Surface: Emergence of a Stable Bridging-Metal Structure Motif

By combining first principles transition state location and molecular dynamics simulation, we unambiguously identify a carbon atom approaching induced bridging metal structure formation on Cu (111) surface, which strongly modify the carbon atom coalescence dynamics. The emergence of this new structural motif turns out to be a result of the subtle balance between Cu-C and Cu-Cu interactions. Based on this picture, a simple theoretical model is proposed, which describes a variety of surface chemistries very well

All-Optical Spiking Neuron Based On Passive Micro-Resonator

Neuromorphic photonics that aims to process and store information simultaneously like human brains has emerged as a promising alternative for the next generation intelligent computing systems. The implementation of hardware emulating the basic functionality of neurons and synapses is the fundamental work in this field. However, previously proposed optical neurons implemented with SOA-MZIs, modulators, lasers or phase change materials are all dependent on active devices and quite difficult for integration. Meanwhile, although the nonlinearity in nanocavities has long been of interest, the previous theories are intended for specific situations, e.g., self-pulsation in microrings, and there is still a lack of systematic studies in the excitability behavior of the nanocavities including the silicon photonic crystal cavities. Here, we report for the first time a universal coupled mode theory model for all side-coupled passive microresonators. Attributed to the nonlinear excitability, the passive microresonator can function as a new type of all-optical spiking neuron. We demonstrate the microresonator-based neuron can exhibit the three most important characteristics of spiking neurons: excitability threshold, refractory period and cascadability behavior, paving the way to realize all-optical spiking neural networks.Comment: 8 pages, 7 figure

Time-dependent dielectric response of polymer nanoparticulate composites containing rapidly oscillating source terms

This thesis presents the derivation of the homogenized equations for the macroscopic response of time-dependent dielectric composites that contain space charges varying spatially at the length scale of the microstructure and that are subjected to alternating electric fields. The focus is on dielectrics with periodic microstructures and two fairly general classes of space charges: passive (or fixed) and active (or locally mobile). With help of a standard change of variables, in spite of the presence of space charges, the derivation amounts to transcribing a previous two-scale-expansion result introduced in Lefevre and Lopez-Pamies (2017a) for perfect dielectrics to the realm of complex frequency-dependent dielectrics. With the objectives of illustrating their use and of showcasing their ability to describe and explain the macroscopic response of emerging materials featuring extreme dielectric behaviors, the derived homogenization results are deployed to examine dielectric spectroscopy experiments on various polymer nanoparticulate composites. It is found that so long as space charges are accounted for, the proposed theoretical results are able to describe and explain all the experimental results. By the same token, more generally, these representative comparisons with experiments point to the manipulation of space charges at small length scales as a promising strategy for the design of materials with exceptional macroscopic properties

Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)

As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput. However, training such LB tasks often meets large generalization gap and downgrades final precision, which limits enlarging the batch size. In this work, we develop the variance reduced gradient descent technique (VRGD) based on the gradient signal to noise ratio (GSNR) and apply it onto popular optimizers such as SGD/Adam/LARS/LAMB. We carry out a theoretical analysis of convergence rate to explain its fast training dynamics, and a generalization analysis to demonstrate its smaller generalization gap on LB training. Comprehensive experiments demonstrate that VRGD can accelerate training ($1\sim 2 \times$), narrow generalization gap and improve final accuracy. We push the batch size limit of BERT pretraining up to 128k/64k and DLRM to 512k without noticeable accuracy loss. We improve ImageNet Top-1 accuracy at 96k by $0.52pp$ than LARS. The generalization gap of BERT and ImageNet training is significantly reduce by over $65\%$.Comment: 25 pages, 5 figure

Direct Solving the Many-Electron Schr\"odinger Equation with a Language Model

The many-electron Schr\"odinger equation is solved straightforwardly with a Transformer-based neural-network architecture (QiankunNet), which requires no external training data and significantly improves the accuracy and efficiency of first-principles calculations compared to previous Fermionic ansatz. The intricate quantum correlations are effectively captured by incorporating the attention mechanism into our methodology. Additionally, the batched sampling strategy is used to significantly improve the sampling accuracy and efficiency. Furthermore, a pre-training stage which incorporates the truncated configuration interaction solution into the variational ansatz, ensuring high expressiveness and further improving computational efficiency. QiankunNet demonstrates the power of the Transformer-based language model in achieving unprecedented efficiency in quantum chemistry calculations, which opens new avenues to chemical discovery and has the potential to solve the large-scale Schr\"odinger equation with modest computational cost

Electrochemical Sensor for o-Nitrophenol Based on β

An electrochemical sensor for the quantification of o-nitrophenol (o-NP) has been developed based on the β-cyclodextrin functionalized graphene nanosheets modified glassy carbon electrode (CD-GNs/GCE). The results indicated that CD-GNs showed good electrochemical behavior to the redox of o-NP which is attributed to the combination of the excellent properties of graphene and cyclodextrin. The peak currents possess a linear relationship with the concentration of o-NP in the range of 5–400 μM. The detection limit of o-NP reached to 0.3 μM on the basis of the signal-to-noise characteristics (S/N=3). The peak potentials for the reversible redox waves are not affected by other nitrophenol isomers (m, p-NP), illustrating good selectivity. Furthermore, the developed electrochemical sensor exhibited good stability and reproducibility for the detection of o-NP and could be used to determine o-NP in real water sample