English

Nexus-CAT: A Computational Framework to Define Long-Range Structural Descriptors in Glassy Materials from Percolation Theory

Disordered Systems and Neural Networks 2026-04-14 v1 Statistical Mechanics

Abstract

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.

Keywords

Cite

@article{arxiv.2604.11476,
  title  = {Nexus-CAT: A Computational Framework to Define Long-Range Structural Descriptors in Glassy Materials from Percolation Theory},
  author = {Julien Perradin and Simona Ispas and Anwar Hasmy and Bernard Hehlen},
  journal= {arXiv preprint arXiv:2604.11476},
  year   = {2026}
}

Comments

31 pages, 7 figures, 1 table

R2 v1 2026-07-01T12:06:26.885Z