# Discovering Phase Transitions with Unsupervised Learning

arXiv: https://arxiv.org/abs/1606.00318 · HTML: https://mlsanity.com/paper/1606.00318/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 30 of 49)

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- [Can a CNN trained on the Ising model detect the phase transition of the q-state Potts model?](https://mlsanity.com/paper/2104.03632/) (2021)
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- [Feature extraction of machine learning and phase transition point of Ising model](https://mlsanity.com/paper/2111.11166/) (2021)
- [Inferring topological transitions in pattern-forming processes with self-supervised learning](https://mlsanity.com/paper/2203.10204/) (2022)
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- [Direct detection of plasticity onset through total-strain profile evolution](https://mlsanity.com/paper/2107.03738/) (2021)
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## Abstract (arXiv)

Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly. We show that unsupervised learning techniques can be readily used to identify phases and phases transitions of many body systems. Starting with raw spin configurations of a prototypical Ising model, we use principal component analysis to extract relevant low dimensional representations the original data and use clustering analysis to identify distinct phases in the feature space. This approach successfully finds out physical concepts such as order parameter and structure factor to be indicators of the phase transition. We discuss future prospects of discovering more complex phases and phase transitions using unsupervised learning techniques.