相关论文: Topological Graph-based Analysis of Solid-State Io…
The congestion formation on a urban road network is one of the key issue for the development of a sustainable mobility in the future smart cities. In this work we propose a reductionist approach studying the stationary states of a simple…
Classical path search assumes complete graphs and scalar optimization metrics, yet real infrastructure networks are incomplete and require multi-dimensional evaluation. We introduce the concept of traversal: a generalization of paths that…
Biological ion channels balance electrostatic and dehydration effects to yield large ion selectivities alongside high transport rates. These macromolecular systems are often interrogated through point mutations of their pore domain,…
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation, modeling and weighting…
We present a mathematically simple procedure for explaining and visualizing the dynamics of quantized transport in topological insulators. The procedure serves to illustrate and clarify the dynamics of topological transport in general, but…
Formation of a molecular network from multifunctional precursors is modelled with a random graph process. The random graph model favours reactivity for monomers that are positioned close in the network topology, and disfavours reactivity…
Three-dimensional dislocation networks control the mechanical properties such as strain hardening of crystals. Due to the complexity of dislocation networks and their temporal evolution, analysis tools are needed that fully resolve the…
Ionic diffusion in solids is central to energy storage, electronics, and catalysis, yet its chemical origins are difficult to resolve because conventional diffusion models struggle with effects of confinement, crystallographic disorder,…
Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for…
Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in…
Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the…
We have developed a steady state theory of complex transport networks used to model the flow of commodity, information, viruses, opinions, or traffic. Our approach is based on the use of the Markov chains defined on the graph…
Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we…
Ionic transport in nano- to sub-nano-scale pores is highly dependent on translocation barriers and potential wells. These features in the free-energy landscape are primarily the result of ion dehydration and electrostatic interactions. For…
Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs…
Solid ionic conductors are essential components of batteries and fuel cells. In many cases, ionic conduction through crystalline materials with substitutional disorder can be modeled with atomic-scale lattice model percolation simulations.…
The available sites for ions in a typical disordered ionic conductor are determined. For this purpose we devised a straightforward algorithm which via cluster analysis identifies these sites from long time ionic trajectories below the glass…
Ionic conduction in crystalline solids is conventionally understood to proceed via atomic-scale defects such as vacancies or interstitials. Here, by addressing the long-standing structural ambiguity of high-temperature tetragonal tantalum…
This monograph introduces key concepts and problems in the new research area of Periodic Geometry and Topology for materials applications.Periodic structures such as solid crystalline materials or textiles were previously classified in…
The progress in understanding the behavior of glassy mixed ionic conductors within the concept of the defect model for the mixed mobile ion effect (V. Belostotsky, J. Non-Cryst. Solids 353 (2007) 1078) is reported. It is shown that in a…