Multi-scale symmetry analysis in molecular structures

TL;DR

This paper introduces a multi-scale symmetry analysis method using persistent automorphism modules within topological data analysis to quantify symmetry variations in complex data, demonstrated through fullerene stability prediction.

Contribution

It develops a novel framework combining automorphism groups and persistent homology to analyze symmetries across scales in data structures.

Findings

Successfully applied to fullerene structures

Achieved a correlation coefficient of 0.979 in stability prediction

Provides a new tool for multi-scale symmetry analysis

Abstract

Topological data analysis (TDA), as a relatively recent approach, has demonstrated great potential in capturing the intrinsic and robust structural features of complex data. While persistent homology, as a core tool of TDA, focuses on characterizing geometric shapes and topological structures, the automorphism groups of Vietoris-Rips complexes can capture the structured symmetry features of data. In this work, we propose a multi-scale symmetry analysis approach that leverages persistent automorphism modules to quantify variations in symmetries across scales. By modifying the category of graphs and constructing a suitable functor from the graph category to the category of modules, we ensure that the persistent automorphism module forms a genuine persistence module. Furthermore, we apply this framework to the structural analysis of fullerenes, predicting the stability of 12 fullerene molecules with a competitive correlation coefficient of 0.979.