Nexus-CAT: A computational framework to define long-range structural descriptors in glassy materials from percolation theory

Nexus-CAT (Cluster Analysis Toolkit) is an open-source Python package for cluster detection and percolation analysis of atomistic simulation trajectories. Standard structural tools, such as the pair distribution function or structure factor, fail to capture the long-range connectivity changes underlying amorphous-amorphous transitions in glassy materials. Nexus-CAT addresses this gap by reading extended XYZ trajectory files and identifying clusters via a Union-Find algorithm with path-compression. Four clustering strategies, i.e., distance-based, bonding, coordination-filtered, and shared-neighbor, are implemented through a Strategy Factory design pattern, enabling the treatment of diverse network topologies. The program computes key percolation properties with percolation detection based on a rigorous period vector algorithm. The package is validated against theoretical predictions and applied to glasses with different bonding environments, namely vitreous silica, vitreous ice, and amorphous silicon. One original result is the observation of a percolation transition prior to crystallization in the latter, indicating that pressure-induced crystallization is initially driven by an amorphous transformation with similar coordination number. The code is also designed to be readily extended to gels, cements, and other disordered materials. Nexus-CAT is fully available on GitHub and PyPI.Nexus-CAT（Cluster Analysis Toolkit，团簇分析工具包）是一款开源Python软件包，用于原子模拟轨迹的团簇检测与渗流分析。诸如径向分布函数（pair distribution function）、结构因子（structure factor）这类常规结构分析工具，无法捕捉玻璃态材料中非晶-非晶转变背后的长程连通性变化。Nexus-CAT通过读取扩展XYZ（extended XYZ）轨迹文件，并借助带路径压缩的并查集（Union-Find）算法识别团簇，填补了这一研究空白。该工具通过策略工厂（Strategy Factory）设计模式实现了四种团簇分析策略，即基于距离、基于成键、基于配位过滤以及基于共享近邻的策略，可适配多样化的网络拓扑结构。程序基于严谨的周期矢量算法完成渗流检测，可计算关键渗流特性。本软件包已通过理论预测验证，并被应用于不同成键环境的玻璃态材料，即熔融石英、玻璃态冰与非晶硅。一项原创性研究发现为：在非晶硅体系中观察到了结晶前的渗流转变，这表明压力诱导的结晶最初由配位数相近的非晶转变所驱动。该代码架构还设计为可便捷扩展至凝胶、水泥及其他无序材料体系。Nexus-CAT可在GitHub与PyPI平台完全获取。