scholarly journals Atomic-scale one-dimensional materials identified from graph theory

Author(s):  
Shunning Li ◽  
Zhefeng Chen ◽  
Zhi Wang ◽  
Mouyi Weng ◽  
Jianyuan Li ◽  
...  

Abstract The past decades have witnessed an exponential growth in the discovery of functional materials, benefited from our unprecedented capabilities in characterizing their structure, chemistry, and morphology with the aid of advanced imaging, spectroscopic and computational techniques. Among these materials, atomic-scale low-dimensional compounds, as represented by the two-dimensional (2D) atomic layers, one-dimensional (1D) atomic chains and zero-dimensional (0D) atomic clusters, have long captivated scientific interest due to their unique topological motifs and exceptional properties. Their tremendous potentials in various applications make it a pressing urgency to establish a complete database of their structural information, especially for the underexplored 1D species. Here we apply graph theory in combination with first-principles high-throughput calculations to identify atomic-scale 1D materials that can be conceptually isolated from their parent bulk crystals. In total, two hundred and fifty 1D atomic chains are shown to be potentially exfoliable. We demonstrate how the lone electron pairs on cations interact with the p-orbitals of anions and hence stabilize their edge sites. Data analysis of the 2D and 1D materials also reveals the dependence of electronic band gap on the cationic percolation network determined by graph theory. The library of 1D compounds systematically identified in this work will pave the way for the predictive discovery of material systems for quantum engineering, and can serve as a source of stimuli for future data-driven design and understanding of functional materials with reduced dimensionality.

2021 ◽  
Vol 236 ◽  
pp. 01028
Author(s):  
Biaohua Wei ◽  
Xu Han

Semiconductor tellurium is an excellent performance material, tellurium and its compounds have been extensive researched in the low-dimensional field. Inspired by the synthesis of a one-dimensional tellurium atomic chains, we predict a new one-dimensional Te2I single-atomic chain structure based on firstprinciples. Using first-principles calculations, Te2I single-atomic chain has an exfoliated energy of 137.95 meV, suggesting that the exfoliation of atomic chains materials from the bulk phase could be feasible. The single-atomic chain structure is an indirect band gap semiconductor with a band gap of 1.51 eV. In addition, its dynamic and thermodynamic properties indicate that the structure is stable at room temperature. Remarkably, it exhibits good electronic conductivity and a large difference in electron and hole mobilities, indicating that it is favorable for the migration and separation of photogenerated carriers. The absorption spectrum of one-dimensional Te2I single-atomic chain exhibits a strong light-harvesting ability in the ultraviolet region, suggesting its potential application in optoelectronic devices


Author(s):  
D. J. Wallis ◽  
N. D. Browning

In electron energy loss spectroscopy (EELS), the near-edge region of a core-loss edge contains information on high-order atomic correlations. These correlations give details of the 3-D atomic structure which can be elucidated using multiple-scattering (MS) theory. MS calculations use real space clusters making them ideal for use in low-symmetry systems such as defects and interfaces. When coupled with the atomic spatial resolution capabilities of the scanning transmission electron microscope (STEM), there therefore exists the ability to obtain 3-D structural information from individual atomic scale structures. For ceramic materials where the structure-property relationships are dominated by defects and interfaces, this methodology can provide unique information on key issues such as like-ion repulsion and the presence of vacancies, impurities and structural distortion.An example of the use of MS-theory is shown in fig 1, where an experimental oxygen K-edge from SrTiO3 is compared to full MS-calculations for successive shells (a shell consists of neighboring atoms, so that 1 shell includes only nearest neighbors, 2 shells includes first and second-nearest neighbors, and so on).


Nano Letters ◽  
2021 ◽  
Author(s):  
Yuan Zhang ◽  
Daniel J. Trainer ◽  
Badri Narayanan ◽  
Yang Li ◽  
Anh T. Ngo ◽  
...  

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.


2021 ◽  
Vol 13 (2) ◽  
pp. 51
Author(s):  
Lili Sun ◽  
Xueyan Liu ◽  
Min Zhao ◽  
Bo Yang

Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on variational (graph) autoencoder assume that the prior of latent variables obeys the standard normal distribution which encourages all nodes to gather around 0. That leads to the inability to fully utilize the latent space. Therefore, it becomes a challenge on how to choose a suitable prior without incorporating additional expert knowledge. Given this, we propose a novel noninformative prior-based interpretable variational graph autoencoder (NPIVGAE). Specifically, we exploit the noninformative prior as the prior distribution of latent variables. This prior enables the posterior distribution parameters to be almost learned from the sample data. Furthermore, we regard each dimension of a latent variable as the probability that the node belongs to each block, thereby improving the interpretability of the model. The correlation within and between blocks is described by a block–block correlation matrix. We compare our model with state-of-the-art methods on three real datasets, verifying its effectiveness and superiority.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Aditya Tandon ◽  
Yong-Yeol Ahn ◽  
Filippo Radicchi

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.


2013 ◽  
Vol 203-204 ◽  
pp. 42-47
Author(s):  
Albert Prodan ◽  
Herman J.P. van Midden ◽  
Erik Zupanič ◽  
Rok Žitko

Charge density wave (CDW) ordering in NbSe3 and the structurally related quasi one-dimensional compounds is reconsidered. Since the modulated ground state is characterized by unstable nano-domains, the structural information obtained from diffraction experiments is to be supplemented by some additional information from a method, able to reveal details on a unit cell level. Low-temperature (LT) scanning tunneling microscopy (STM) can resolve both, the local atomic structure and the superimposed charge density modulation. It is shown that the established model for NbSe3 with two incommensurate (IC) modes, q1 = (0,0.241,0) and q2 = (0.5,0.260,0.5), locked in at T1=144K and T2=59K and separately confined to two of the three available types of bi-capped trigonal prismatic (BCTP) columns, must be modified. The alternative explanation is based on the existence of modulated layered nano-domains and is in good accord with the available LT STM results. These confirm i.a. the presence of both IC modes above the lower CDW transition temperature. Two BCTP columns, belonging to a symmetry-related pair, are as a rule alternatively modulated by the two modes. Such pairs of columns are ordered into unstable layered nano-domains, whose q1 and q2 sub-layers are easily interchanged. The mutually interchangeable sections of the two unstable IC modes keep a temperature dependent long-range ordering. Both modes can formally be replaced by a single highly inharmonic long-period commensurate CDW.


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