# Probing hidden spin order with interpretable machine learning

arXiv: https://arxiv.org/abs/1804.08557 · HTML: https://mlsanity.com/paper/1804.08557/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 8 of 8)

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- [Fluctuation based interpretable analysis scheme for quantum many-body snapshots](https://mlsanity.com/paper/2304.06029/) (2023)
- [Quantitative analysis of phase transitions in two-dimensional XY models using persistent homology](https://mlsanity.com/paper/2109.10960/) (2021)
- [Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets](https://mlsanity.com/paper/2102.01103/) (2021)
- [TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data](https://mlsanity.com/paper/2609.20693/) (2026)
- [Autonomous phase discovery](https://mlsanity.com/paper/2609.33802/) (2026)

## Abstract (arXiv)

The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids and spin nematics. However, discerning their existence and the correct characterization is usually challenging. Here we introduce a machine-learning protocol that can identify general nematic order and their order parameter from seemingly featureless spin configurations, thus providing comprehensive insight on the presence or absence of hidden orders. We demonstrate the capabilities of our method by extracting the analytical form of nematic order parameter tensors up to rank 6. This may prove useful in the search for novel spin states and for ruling out spurious spin liquid candidates.