Interfacial Engineering of Flexible Transparent Conducting Films

One-dimensional (1D) carbon nanotubes (CNTs) and silver nanowires (AgNWs) have been used as replacements for brittle indium tin oxide (ITO) in the fabrication of transparent conducting films (TCFs), which can be used in opto-electronic devices such as screen panels, solar cell panels, and organic light-emitting diodes. This chapter describes a fabrication method of high-performance TCFs by solution processing of single-walled CNTs (SWCNTs) and AgNWs. Highly uniform TCFs with SWCNTs and AgNW inks were fabricated using spray deposition. Their performance was modulated by interfacial engineering such as overcoating with silane compound for densification of SWCNT networks and chemical or photothermal welding of SWCNT networks on thermoplastic substrates. Moreover, the hybridization of SWCNTs, AgNWs, and graphene oxide nanosheets is a promising approach to mitigate their drawbacks via p-type doping, electrical stabilization, or interfacial stabilization on plastic substrates. The rational control of 1D material networks can provide a good opportunity to fabricate high-performance TCFs for flexible opto-electronic devices

Correlated electronic states at domain walls of a Mott-charge-density-wave insulator 1T-TaS2

Domain walls in interacting electronic systems can have distinct localized states, which often govern physical properties and may lead to unprecedented functionalities and novel devices. However, electronic states within domain walls themselves have not been clearly identified and understood for strongly correlated electron systems. Here, we resolve the electronic states localized on domain walls in a Mott-charge-density-wave(CDW) insulator 1T-TaS2 using scanning tunneling spectroscopy. We establish that the domain wall state decomposes into two nonconducting states located at the center of domain walls and edges of domains. Theoretical calculations reveal their atomistic origin as the local reconstruction of domain walls under the strong influence of electron correlation. Our results introduce a concept for the domain wall electronic property, the wall's own internal degrees of freedom, which is potentially related to the controllability of domain wall electronic properties

Zoology of domain walls in quasi-2D correlated charge density wave of 1T-TaS2

Domain walls in correlated charge density wave compounds such as 1T-TaS2 can have distinct localized states which govern physical properties and functionalities of emerging quantum phases. However, detailed atomic and electronic structures of domain walls have largely been elusive. We identify using scanning tunneling microscope and density functional theory calculations the atomic and electronic structures for a plethora of discommensuration domain walls in 1T-TaS2 quenched metastably with nanoscale domain wall networks. The domain walls exhibit various in-gap states within the Mott gap but metallic states appear in only particular types of domain walls. A systematic understanding of the domain-wall electronic property requests not only the electron counting but also including various intertwined interactions such as structural relaxation, electron correlation, and charge transfer. This work guides the domain wall engineering of the functionality in correlated van der Waals materials.Comment: 7 pages, 4 figure

Mobile Kink Solitons in a Van der Waals Charge-Density-Wave Layer

Kinks, point-like geometrical defects along dislocations, domain walls, and DNA, are stable and mobile, as solutions of a sine-Gordon wave equation. While they are widely investigated for crystal deformations and domain wall motions, electronic properties of individual kinks have received little attention. In this work, electronically and topologically distinct kinks are discovered along electronic domain walls in a correlated van der Waals insulator of 1$T$-TaS$_2$. Mobile kinks and antikinks are identified as trapped by pinning defects and imaged in scanning tunneling microscopy. Their atomic structures and in-gap electronic states are unveiled, which are mapped approximately into Su-Schrieffer-Heeger solitons. The twelve-fold degeneracy of the domain walls in the present system guarantees an extraordinarily large number of distinct kinks and antikinks to emerge. Such large degeneracy together with the robust geometrical nature may be useful for handling multilevel information in van der Waals materials architectures.Comment: 12 pages, 4 figure

Grouping-matrix based Graph Pooling with Adaptive Number of Clusters

Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assume that all input graphs share the same number of clusters. In inductive settings where the number of clusters can vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.Comment: 10 pages, 3 figure

Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (GATE). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that GATE outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.Comment: 12+11 pages, 6+1 figures, 0+7 table