partycls: A Python package for structural clustering
Computational Physics
2021-11-22 v1 Disordered Systems and Neural Networks
Materials Science
Abstract
partycls is a Python framework for cluster analysis of systems of interacting particles. By grouping particles that share similar structural or dynamical properties, partycls enables rapid and unsupervised exploration of the system's relevant features. It provides descriptors suitable for applications in condensed matter physics and integrates the necessary tools of unsupervised learning, such as dimensionality reduction, into a streamlined workflow. Through a simple and expressive interface, partycls allows one to open a trajectory file, perform a clustering based on the selected structural descriptor, and analyze and save the results with only a few lines of code.
Keywords
Cite
@article{arxiv.2111.10133,
title = {partycls: A Python package for structural clustering},
author = {Joris Paret and Daniele Coslovich},
journal= {arXiv preprint arXiv:2111.10133},
year = {2021}
}
Comments
6 pages, 3 figures