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Machine learning has brought great convenience to material property prediction. However, most existing models can only predict properties of molecules or crystals with specific size, and usually only local atomic environment or molecular global descriptor representation be used as the characteristics of the model, resulting in poor model versatility and cannot be applied to multiple systems. We propose a method that combines the description of the local atomic environment and the overall structure of the molecule, a fusion model consisting of a graph convolutional neural network and a fully connected neural network is used to predict the properties of molecules or crystals, and successfully applied to QM9 organic molecules and semiconductor crystal materials. Our method is not limited to a specific size of a molecule or a crystal structure. According to the calculation principle of the properties of the material molecules, the influences of the local atomic environment and the overall structure of the molecules on the properties are respectively considered, an appropriate weighting ratio is selected to predict the properties. As a result, the prediction performance has been greatly improved. In fact, the proposed method is not limited to organic molecules and crystals and is also applicable to other structures, such as clusters.

Renal metabolism is essential for kidney functions and energy homeostasis in the body. The TCA cycle is the hub of metabolism, but the metabolic activities of the cycle in the kidney have rarely been investigated. This study is to assess metabolic processes at the level of the TCA cycle in the kidney based on isotopomer distributions in multiple metabolites. Isolated rat kidneys were perfused with media containing common substrates including lactate and alanine for an hour. One group of kidneys received [U‐13C3]lactate instead of natural abundance lactate while the other group received [U‐13C3]alanine instead of natural abundance alanine. Perfused kidneys and effluent were prepared for analysis using NMR spectroscopy. 13C‐labeling patterns in glutamate, fumarate, aspartate and succinate from the kidney extracts showed that pyruvate carboxylase and oxidative metabolism through the TCA cycle were comparably very active, but pyruvate cycling and pyruvate dehydrogenase were relatively less active. Isotopomer analyses with fumarate and malate from effluent, however, indicated that pyruvate carboxylase was much more active than the TCA cycle and other metabolic processes. The reverse equilibrium of oxaloacetate with four‐carbon intermediates of the cycle was nearly complete (92%), based on the ratio of [2,3,4‐13C3]/[1,2,3‐13C3] in aspartate or malate. 13C enrichment in glucose with 13C‐lactate supply was higher than that with 13C‐alanine. Isotopomer analyses with multiple metabolites (i.e., glutamate, fumarate, aspartate, succinate and malate) allowed us to assess relative metabolic processes in the TCA cycle in the kidney supplied with [U‐13C3]lactate. Data from the analytes were generally consistent, indicating highly active pyruvate carboxylase and oxidative metabolism through the TCA cycle. Different 13C‐labeling patterns in analytes from the kidney extracts versus effluent suggested metabolic compartmentalization. [ABSTRACT FROM AUTHOR]

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C7O2H2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (AlxGayInz)2O3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features. [ABSTRACT FROM AUTHOR]

Aiming at the insulation of low-frequency acoustic sound, a 100 mm crescent disc asymmetric membrane-type acoustic metamaterial structure was designed in this paper, which was composed of aluminum material as the frame and iron material as the mass attached to the surface of flexible ethylene-vinyl acetate copolymer film. The finite element method was adopted to calculate its transmission loss and displacement field. The asymmetric structure, the structure parameters and the mass block's eccentricity together with the vibrational modes analysis were investigated in this study for a better sound insulation performance. The results show that, compared with the symmetric membrane-type acoustic metamaterials, the design of the asymmetry in a single cell makes the low-frequency sound insulation band widened by 23 Hz. Meanwhile, more vibrational modes are generated which illustrates that the coexistence of Lorentz resonance and Fano resonance promotes a better sound insulation performance of the crescent disc asymmetric structure. The large-size asymmetric membrane-type acoustic metamaterial structure designed in this paper can reduce the sound insulation frequency to 10 Hz with a wide low-frequency sound insulation performance within 10 ~ 500 Hz. It provides a new method for improving the low-frequency sound insulation effect of sound barriers in terms of structural optimization design. [ABSTRACT FROM AUTHOR]

Achieving scalable synthesis of nanoscale transition‐metal carbides (TMCs), regarded as substitutes for platinum‐group noble metals, remains an ongoing challenge. Herein, a 100‐g scale synthesis of single‐phased cobalt carbide (Co2C) through carburization of Co‐based Prussian Blue Analog (Co‐PBA) is reported in CO2/H2 atmosphere under mild conditions (230 °C, ambient pressure). Textural property investigations indicate a successful preparation of orthorhombic‐phased Co2C nanomaterials with Pt‐group–like electronic properties. As a demonstration, Co2C achieves landmark photo‐assisted thermal catalytic CO2 conversion rates with photo‐switched product selectivity, which far exceeds the representative Pt‐group‐metal–based catalysts. This impressive result is attributed to the excellent activation of reactants, colorific light absorption, and photo‐to‐thermal conversion capacities. In addition to CO2 hydrogenation, the versatile Co2C materials show huge prospects in antibacterial therapy, interfacial water evaporation, electrochemical hydrogen evolution reaction, and battery technologies. This study paves the way toward unlocking the potential of multi‐functional Co2C nanomaterials. [ABSTRACT FROM AUTHOR]