Identifying clusters on a discrete periodic lattice via machine learning
Abstract
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.
- Publication:
-
Computer Physics Communications
- Pub Date:
- October 2019
- DOI:
- 10.1016/j.cpc.2019.05.004
- arXiv:
- arXiv:1901.00091
- Bibcode:
- 2019CoPhC.243..106L
- Keywords:
-
- Hierarchical clustering;
- Lattice simulations;
- Breadth-first search;
- Periodic boundary conditions;
- Physics - Computational Physics;
- Quantitative Biology - Quantitative Methods
- E-Print:
- 11 pages, 2 figures