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Recent years, the research on object classification based on three-dimensional (3D) point cloud pays more attention to extract the features from point sets directly. PointNet++ is the latest network structure for 3D classification which has achieved acceptable results. Although it has achieved acceptable results, there are still two problems: (i) The farthest point sampling (FPS) algorithm in PointNet++ ignores the fact that the feature of each point contributes differently to the classification and segmentation tasks. Therefore, FPS cannot guarantee that the selected point sets can correctly represent the main features of the object. (ii) The multi-scale grouping and multi-resolution grouping in PointNet++ do not consider the features between different levels of the same point. In order to solve these problems, the authors propose the point-selection structure which can calculate the importance of each point's feature. In addition, multi-level-point-feature fusion module is proposed to extract the features of the point at all levels and fuse them as a new feature of that point. In this Letter, they make some experiments on ModelNet40 and ScanNet datasets, which achieves better results compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Density functional theory calculations are employed to systematically investigate the trend of hydrogen evolution reaction (HER) performance of oxygen-terminated MXenes. By studying 30 transition-metal carbides and 30 transition-metal nitrides, Mn+1CnO2 and Mn+1NnO2 (M = Sc, Cr, Hf, Mo, Nb, Ta, Ti, V, W, Zr; n = 1, 2, 3), the tendency of oxygen desorption after hydrogen adsorption is elucidated to play a key role in HER performance of oxygen-terminated MXenes. On the basis of these observations, we propose a suitable HER descriptor, oxygen vacancy formation energy (Ef), which scales linearly with the adsorption free energy of hydrogen, ΔGH. In addition, this new descriptor is linearly correlated with the lithium binding strength on oxygen-terminated MXenes. Therefore, Ef is a universal descriptor for identifying the trend of adsorption processes where adsorbed species donate electrons to oxygen-terminated MXenes. This work provides a general guideline for large-scale screening of promising MXene-based materials for energy storage and conversion. [ABSTRACT FROM AUTHOR]