2026-10-15T21:42:16Z https://data.mendeley.com/oai

oai:data.mendeley.com/w7rcv4tbtn.1 2019-05-24T15:00:48Z

Ballantyne, John Identifying clusters on a discrete periodic lattice via machine learning Mendeley Data Given the ubiquity of lattice models in physics, it is imperative for researchers to possess robust methods for quantifying clusters on the lattice — whether they be Ising spins or clumps of molecules. Inspired by biophysical studies, we present Python code for handling clusters on a 2D periodic lattice. Properties of individual clusters, such as their area, can be obtained with a few function calls. Our code invokes an unsupervised machine learning method called hierarchical clustering, which is simultaneously effective for the present problem and simple enough for non-experts to grasp qualitatively. Moreover, our code transparently merges clusters neighboring each other across periodic boundaries using breadth-first search (BFS), an algorithm well-documented in computer science pedagogy. The fact that our code is written in Python – instead of proprietary languages – further enhances its value for reproducible science. Computational Physics Law, Everest Dataset doi:10.17632/w7rcv4tbtn.1 oai:data.mendeley.com/w7rcv4tbtn.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/w7rcv4tbtn 2019-05-24T15:00:48Z