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The dynamics in the photosphere is governed by the multi-scale turbulent convection termed as granulation and supergranulation. It is important to derive 3-dimensional velocity vectors to understand the nature of the turbulent convection. However, it is difficult to obtain the velocity component perpendicular to the line-of-sight, which corresponds to the horizontal velocity in disk center observations. We developed a convolutional neural network model with a multi-scale deep learning architecture. The method consists of multiple convolutional kernels with various sizes of the receptive fields, and it performs convolution for spatial and temporal axes. The network is trained with data from three different numerical simulations of turbulent convection, and we introduced a coherence spectrum to assess the horizontal velocity fields that were derived at each spatial scale. The multi-scale deep learning method successfully predicts the horizontal velocities for each convection simulation in terms of the global-correlation-coefficient, which is often used for evaluating the prediction accuracy of the methods. The coherence spectrum reveals the strong dependence of the correlation coefficients on the spatial scales. Although coherence spectra are higher than 0.9 for large-scale structures, they drastically decrease to less than 0.3 for small-scale structures wherein the global-correlation-coefficient indicates a high value of approximately 0.95. We determined that this decrease in the coherence spectrum occurs around the energy injection scales. The accuracy for the small-scale structures is not guaranteed solely by the global-correlation-coefficient. To improve the accuracy in small-scales, it is important to improve the loss function for enhancing the small-scale structures and to utilize other physical quantities related to the non-linear cascade of convective eddies as input data., Comment: 9 pages, 5 figures, accepted for publication in A&A

To understand the role of phosphate ester dispersant, we investigated the rheology of a BaTiO$_3$ slurry. For the model case, a coarse-grain molecular dynamics (CGMD) simulation was performed with the butyral polymer didodecyl hydrogen phosphate (DHP), in the toluene/ethanol solvent. By systematically analyzing the effect of DHP from an atomic-scale first principle and from all-atom MD to micro-scale CGMD simulation, we investigated how the adsorption of a DHP dispersant on a BaTiO$_3$ surface affects the microstructure rheology of a BaTiO$_3$ slurry. The first-principle and all-atom MD simulation suggests that DHP molecules prefer to locate near the BaTiO$_3$ surface. CGMD simulation shows a reduction in viscosity with an increase in dispersants, suggesting that the dispersant population near the BaTiO$_3$ surface plays a key role in controlling the rheology of the BaTiO$_3$ slurry. In this study, we propose an approach for understanding the BaTiO$_3$ slurry with molecular-level simulations, which would be a useful tool for efficient optimization of slurry preparation., Comment: 28 page 7 figure

Quantum annealing (QA) has gained considerable attention because it can be applied to combinatorial optimization problems, which have numerous applications in logistics, scheduling, and finance. In recent years, research on solving practical combinatorial optimization problems using them has accelerated. However, researchers struggle to find practical combinatorial optimization problems, for which quantum annealers outperform other mathematical optimization solvers. Moreover, there are only a few studies that compare the performance of quantum annealers with one of the most sophisticated mathematical optimization solvers, such as Gurobi and CPLEX. In our study, we determine that QA demonstrates better performance than the solvers in the break minimization problem in a mirrored double round-robin tournament (MDRRT). We also explain the desirable performance of QA for the sparse interaction between variables and a problem without constraints. In this process, we demonstrate that the break minimization problem in an MDRRT can be expressed as a 4-regular graph. Through computational experiments, we solve this problem using our QA approach and two-integer programming approaches, which were performed using the latest quantum annealer D-Wave Advantage, and the sophisticated mathematical optimization solver, Gurobi, respectively. Further, we compare the quality of the solutions and the computational time. QA was able to determine the exact solution in 0.05 seconds for problems with 20 teams, which is a practical size. In the case of 36 teams, it took 84.8 s for the integer programming method to reach the objective function value, which was obtained by the quantum annealer in 0.05 s. These results not only present the break minimization problem in an MDRRT as an example of applying QA to practical optimization problems, but also contribute to find problems that can be effectively solved by QA., Comment: 12pages, 2 figures

Combination of low-dimensionality and electron correlation is vital for exotic quantum phenomena such as the Mott-insulating phase and high-temperature superconductivity. Transition-metal dichalcogenide (TMD) 1T-TaS2 has evoked great interest owing to its unique nonmagnetic Mott-insulator nature coupled with a charge-density-wave (CDW). To functionalize such a complex phase, it is essential to enhance the CDW-Mott transition temperature TCDW-Mott, whereas this was difficult for bulk TMDs with TCDW-Mott < 200 K. Here we report a strong-coupling 2D CDW-Mott phase with a transition temperature onset of ~530 K in monolayer 1T-TaSe2. Furthermore, the electron correlation derived lower Hubbard band survives under external perturbations such as carrier doping and photoexcitation, in contrast to the bulk counterpart. The enhanced Mott-Hubbard and CDW gaps for monolayer TaSe2 compared to NbSe2, originating in the lattice distortion assisted by strengthened correlations and disappearance of interlayer hopping, suggest stabilization of a likely nonmagnetic CDW-Mott insulator phase well above the room temperature. The present result lays the foundation for realizing monolayer CDW-Mott insulator based devices operating at room temperature.

Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies show that when 3D molecular geometries, such as bond lengths and angles, are available, molecular property prediction tasks can be made more accurate. However, computing of 3D molecular geometries requires quantum calculations that are computationally prohibitive. For example, accurate calculation of 3D geometries of a small molecule requires hours of computing time using density functional theory (DFT). Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods. To make this feasible, we develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules derived from DFT. We also provide a set of software tools for data processing, splitting, training, and evaluation, etc. Specifically, we propose to assess the error and validity of predicted geometries using four metrics. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Experimental results show that, compared with generating 3D geometries with RDKit, our method can achieve comparable prediction accuracy but with much smaller computational costs. Our Molecule3D is available as a module of the MoleculeX software library (https://github.com/divelab/MoleculeX).

Combining advanced technologies of optics and antiferromagnetic spintronics, we present a method to realize ultrafast spin transport. The optical Barnett effect provokes quasiequilibrium Bose-Einstein condensates (BECs) of magnons associated with the fully spin-polarized state in insulating antiferromagnets (AFs). This optomagnonic Barnett effect enables us to exploit coherent magnons of high frequency over the conventional ones of (sub-) terahertz associated with the N\'eel magnetic order. We show that the macroscopic coherence of those optical magnon BECs induces a spin current across the junction interface of weakly coupled two insulating AFs, and this optomagnonic Josephson effect realizes ultrafast spin transport. The period of the optomagnonic Josephson oscillation is much shorter than the conventional one of the order of picoseconds. Thus we propose a way to realize ultrafast spin transport in AFs by means of the macroscopic coherence of optical magnon BECs., Comment: 6+6 pages, 2+1 figures, 3 tables

The MPLAPACK (formerly MPACK) is a multiple-precision version of LAPACK (https://www.netlib.org/lapack/). MPLAPACK version 2.0.1 is based on LAPACK version 3.9.1 and translated from Fortran 90 to C++ using FABLE, a Fortran to C++ source-to-source conversion tool (https://github.com/cctbx/cctbx_project/tree/master/fable/). MPLAPACK version 2.0.1 provides the real and complex version of MPBLAS, and the real and complex versions of MPLAPACK support all LAPACK features: solvers for systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value problems, and related matrix factorizations except for mixed-precision routines. The MPLAPACK defines an API for numerical linear algebra, similar to LAPACK. It is easy to port legacy C/C++ numerical codes using MPLAPACK. MPLAPACK supports binary64, binary128, FP80 (extended double), MPFR, GMP, and QD libraries (double-double and quad-double). Users can choose MPFR or GMP for arbitrary accurate calculations, double-double or quad-double for fast 32 or 64-decimal calculations. We can consider the binary64 version as the C++ version of LAPACK. Moreover, it comes with an OpenMP accelerated version of MPBLAS for some routines and CUDA (A100 and V100 support) for double-double versions of Rgemm and Rsyrk. The peak performances of the OpenMP version are almost proportional to the number of cores, and the performances of the CUDA version are impressive, and approximately 400-600 GFlops. MPLAPACK is available at GitHub (https://github.com/nakatamaho/mplapack/) under the 2-clause BSD license.

In order to study the possible superconductivity at the polar surfaces of 1111-type iron-based superconductors, which is doped with a large amount of holes in spite of the electron doping in bulk materials, we have performed angle-resolved photoemission spectroscopy (ARPES) studies on superconducting PrFeAsO$_{1-y}$~crystals. We have indeed observed the opening of a superconducting gap on surface-derived hole pockets as well as on a bulk-derived hole pocket. The superconducting gap is found to open on the surface-derived hole pockets below the bulk $T_c$, which suggests that the surface superconductivity is possibly induced by proximity effect from the bulk. We have also observed the opening of a large pseudogap on the surface-derived hole pockets, which is similar to the pseudogap in 122-type bulk superconductors doped with a smaller amount of holes. This suggests that the opening of a large pseudogap is a characteristic property of hole-doped iron-based superconductors., Comment: Accepted in Physical Review Research