Identifying clusters on a discrete periodic lattice via machine learning
| Main Author: | Ballantyne, John |
|---|---|
| Other Authors: | Law, Everest |
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2019
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| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/w7rcv4tbtn |
Daftar Isi:
- 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.