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Topological magnetic materials are expected to show multiple transport responses because of their unusual bulk electronic topology in momentum space and topological spin texture in real space. However, such multiple topological properties-hosting materials are rare in nature. In this work, we reveal the coexistence of a large tunable anomalous Hall effect and topological magnetic skyrmions in a Weyl nodal ring ferromagnet Mn5Ge3, by using electrical transport and Lorentz transmission electronic microscope (TEM) measurements. It was found that the intrinsic anomalous Hall conductivity (AHC) can reach up to 979.7 S/cm with current along [120] and magnetic field along [001] of the Mn5Ge3 single crystals. Our theoretical calculations reveal that the large AHC is closely related with two Weyl nodal rings in band structure near the Fermi level and is strongly modified by the content of Ge. Moreover, our Lorentz-TEM images and micromagnetic simulation results, together with the sizable topological Hall effect clearly point to the robust formation of magnetic skyrmions over a wide temperature-magnetic field region. These results prove Mn5Ge3 as a rare magnetic topological nodal-line semimetal with great significance to explore novel multiple topological phenomena, which facilitates the development of spintronics., Comment: 38 pages, 22 figures

The recent discoveries of surperisingly large anomalous Hall effect in chiral antiderromagnets have triggered extensive research efforts in various fields, ranging from topological condensed-matter physics to antiferromagnetic spintronics, and energy harvesting technology. However, such AHE-hosting antiferromagnetic materials are rare in nature. Herein, we demonstrate that Mn2.4Ga, a Fermi-level-tuned kagome antiferromagnet, has a large anomalous Hall conductivity of about 150 {\Omega}-1cm-1 at room temperature that surpasses the usual high values (i.e.,20-50 {\Omega}-1cm-1) observed so far in two outstanding kagome antiferromagnets, Mn3Sn and Mn3Ge. The spin triangular structure of Mn2.4Ga guarantees a nonzero Berry curvature while generates only a weak net moment in the kagome plane.Moreover, the anomalous Hall conductivity exhibits a sign reversal with the rotation of a small magnetic field, which can be ascribed to the field-controlled chirality of the spin triangular structure. Our theoretical calculation indicate that the large AHE in Mn2.4Ga originates from a significantly enhanced Berry curvature associated wiht the tuning of the Fermi level close to the Weyl points. These properties, together with the ability to manipulate moment orientations using a moderate external magnetic field, make Mn2.4Ga extremely exciting for future antiferromagnetic spintronics., Comment: This article has been accepted by the Advanced Functional Materials

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors.

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods., Comment: 9 pages, 6 figures