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The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.

In this paper, a 20-GHz-band 64 × 64 hollow waveguide Butler matrix is proposed. By using two-plane short slot couplers and a modified diagram, a short-axis two dimensional Butler matrix is realized. The design method of the diagram and components is presented. The Butler matrix is composed of two plane hybrids, cross couplers and phase shifters. The designed Butler matrix is fabricated by milling and screwing together nine aluminum plates. The dimensions and weight are 86.40 mm × 74.19 mm × 396.34 mm and 7.0 kg, respectively. Transmission characteristics as a beam switching circuit are characterized by measurements, with a low insertion loss of less than 1.8 dB and an excitation error of 4-dB and 40-deg. standard deviation. Radiation characteristics as a 64-beam antenna are measured, and a wide coverage area of 40% of the hemisphere and high directivity of more than 18.7 dBi were determined by measurements. INDEX TERMS Butler matrix, hollow waveguide, two dimensional beam switching, two-plane short slot coupler.

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Quantum‐dot (QD) photovoltaics (PVs) offer promise as energy‐conversion devices; however, their open‐circuit‐voltage (VOC) deficit is excessively large. Previous work has identified factors related to the QD active layer that contribute to VOC loss, including sub‐bandgap trap states and polydispersity in QD films. This work focuses instead on layer interfaces, and reveals a critical source of VOC loss: electron leakage at the QD/hole‐transport layer (HTL) interface. Although large‐bandgap organic materials in HTL are potentially suited to minimizing leakage current, dipoles that form at an organic/metal interface impede control over optimal band alignments. To overcome the challenge, a bilayer HTL configuration, which consists of semiconducting alpha‐sexithiophene (α‐6T) and metallic poly(3,4‐ethylenedioxythiphene) polystyrene sulfonate (PEDOT:PSS), is introduced. The introduction of the PEDOT:PSS layer between α‐6T and Au electrode suppresses the formation of undesired interfacial dipoles and a Schottky barrier for holes, and the bilayer HTL provides a high electron barrier of 1.35 eV. Using bilayer HTLs enhances the VOC by 74 mV without compromising the JSC compared to conventional MoO3 control devices, leading to a best power conversion efficiency of 9.2% (>40% improvement relative to relevant controls). Wider applicability of the bilayer strategy is demonstrated by a similar structure based on shallow lowest‐unoccupied‐molecular‐orbital (LUMO) levels.

Despite the amorphous nature of glassy water, x-ray or neutron scattering experiments reveal sharp peaks in the structure factor, indicating the existence of medium-range order (MRO) in the system. However the real space origin of the peaks has yet to be disclosed. Herein, we use a combined approach of molecular dynamics simulations and persistent homology (PH) to investigate two types of glassy water, low-density amorphous (LDA) and high-density amorphous (HDA) ices. We present prominent MRO ring structures in each type of the ices, distinguished by their size and shape as well as the number of their components: MRO rings in HDA are observed smaller, less planar and more membered, compared to those in LDA. The PH-extracted MRO rings successfully reproduce the quantitative features, including the position and width, of the first sharp diffraction peaks in the structure factor, hence suitably serving as the origin of experimental MRO signatures in the amorphous ices. Our study supports that PH is an effective tool to identify hidden MRO in amorphous configurations.

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory (DFT) is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs) involving at least thousands of noble metal atoms, and this limitation calls for machine learning (ML)-driven approaches. Herein, with the aim of accelerating the accurate prediction of adsorption energies for a wide range of surface coverages on large-size NPs, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the much enhanced accuracy of the bond-type embedding approach compared to the original CGCNN, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6,525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. We reveal that ML-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size, such as the increasing O- to OH-covered phase ratio and the decreasing Pt dissolution phase in the diagrams. This work suggests a new method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.