We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful method to explore new and unknown phases of matter.
@article{arxiv.2205.04051,
title = {Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks},
author = {Zakaria Patel and Ejaaz Merali and Sebastian J. Wetzel},
journal= {arXiv preprint arXiv:2205.04051},
year = {2022}
}