Probing hidden spin order with interpretable machine learning

TL;DR

This paper presents a machine learning approach that detects hidden nematic spin orders from spin configurations, aiding the discovery and characterization of unconventional magnetic states.

Contribution

It introduces an interpretable machine learning protocol capable of identifying nematic order parameters up to rank 6 from featureless spin data.

Findings

Successfully extracts analytical nematic order parameters.

Identifies hidden spin orders in frustrated magnetism.

Assists in distinguishing true spin liquids from spurious candidates.

Abstract

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.