Persistent Homology for Structural Characterization in Disordered Systems

Autor:	Wang, An, Zou, Li
Rok vydání:	2024
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Materials Science Computer Science - Machine Learning Mathematical Physics 55N31 62R40 I.3.5
Druh dokumentu:	Working Paper
Popis:	We propose a unified framework based on persistent homology (PH) to characterize both local and global structures in disordered systems. It can simultaneously generate local and global descriptors using the same algorithm and data structure, and has shown to be highly effective and interpretable in predicting particle rearrangements and classifying global phases. Based on this framework, we define a non-parametric metric, the Separation Index (SI), which not only outperforms traditional bond-orientational order parameters in phase classification tasks but also establishes a connection between particle environments and the global phase structure. Our methods provide an effective framework for understanding and analyzing the properties of disordered materials, with broad potential applications in materials science and even wider studies of complex systems. Comment: 19 pages, 17 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.14390 Zobrazit plný text záznamu View this record from Arxiv