Density functional calculations of the electronic structure and magnetic properties of the hydrocarbon K3picene superconductor near the metal-insulator transition

We have investigated the electronic structures and magnetic properties of of K3picene, which is a first hydrocarbon superconductor with high transition temperature T_c=18K. We have shown that the metal-insulator transition (MIT) is driven in K3picene by 5% volume enhancement with a formation of local magnetic moment. Active bands for superconductivity near the Fermi level E_F are found to have hybridized character of LUMO and LUMO+1 picene molecular orbitals. Fermi surfaces of K3picene manifest neither prominent nesting feature nor marked two-dimensional behavior. By estimating the ratio of the Coulomb interaction U and the band width W of the active bands near E_F, U/W, we have demonstrated that K3picene is located in the vicinity of the Mott transition.Comment: 5 pages, 5 figure

Effect of sintering temperature under high pressure in the uperconductivity for MgB2

We report the effect of the sintering temperature on the superconductivity of MgB2 pellets prepared under a high pressure of 3 GPa. The superconducting properties of the non-heated MgB2 in this high pressure were poor. However, as the sintering temperature increased, the superconducting properties were vastly enhanced, which was shown by the narrow transition width for the resistivity and the low-field magnetizations. This shows that heat treatment under high pressure is essential to improve superconducting properties. These changes were found to be closely related to changes in the surface morphology observed using scanning electron microscopy.Comment: 3 Pages including 3 figure

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table