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碲化铁网络中的拓扑指数和模式。

Topological indices and patterns in iron telluride networks.

作者信息

Yang Hong, Hanif Muhammad Farhan, Siddiqui Muhammad Kamran, Hanif Muhammad Faisal, Ahmed Hira, Fufa Samuel Asefa

机构信息

School of Computer Science, Chengdu University, Chengdu, China.

Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Lahore, Pakistan.

出版信息

Sci Rep. 2024 Jun 21;14(1):14297. doi: 10.1038/s41598-024-65205-y.

DOI:10.1038/s41598-024-65205-y
PMID:38906950
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11192961/
Abstract

This paper explores the complex interplay between topological indices and structural patterns in networks of iron telluride (FeTe). We want to analyses and characterize the distinct topological features of (FeTe) by utilizing an extensive set of topological indices. We investigate the relationship that these indicators have with the network's physical characteristics by employing sophisticated statistical techniques and curve fitting models. Our results show important trends that contribute to our knowledge of the architecture of the (FeTe) network and shed light on its physiochemical properties. This study advances the area of material science by providing a solid foundation for using topological indices to predict and analyses the behavior of intricate network systems. More preciously, we study the topological indices of iron telluride networks, an artificial substance widely used with unique properties due to its crystal structure. We construct a series of topological indices for iron telluride networks with exact mathematical analysis and determine their distributions and correlations using statistical methods. Our results reveal significant patterns and trends in the network structure when the number of constituent atoms increases. These results shed new light on the fundamental factors that influence material behavior, thus offering a deeper understanding of the iron telluride network and may contribute to future research and engineering of these materials.

摘要

本文探讨了碲化铁（FeTe）网络中拓扑指数与结构模式之间的复杂相互作用。我们希望通过使用大量的拓扑指数来分析和表征（FeTe）的独特拓扑特征。我们运用复杂的统计技术和曲线拟合模型来研究这些指标与网络物理特性之间的关系。我们的结果显示出重要趋势，有助于我们了解（FeTe）网络的结构，并揭示其物理化学性质。本研究通过为使用拓扑指数预测和分析复杂网络系统的行为提供坚实基础，推动了材料科学领域的发展。更确切地说，我们研究了碲化铁网络的拓扑指数，碲化铁是一种因其晶体结构而具有独特性质且被广泛使用的人工物质。我们通过精确的数学分析为碲化铁网络构建了一系列拓扑指数，并使用统计方法确定它们的分布和相关性。我们的结果揭示了当组成原子数量增加时网络结构中的显著模式和趋势。这些结果为影响材料行为的基本因素提供了新的见解，从而加深了对碲化铁网络的理解，并可能有助于这些材料未来的研究和工程应用。

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