Deep Learning of Phase Transitions for Bilinear-Biquadratic Spin-1 Chains from Correlation Aspects
Abstract
We investigate the role of different correlation functions in reconstructing the phase diagram of the spin-1 bilinear-biquadratic chain, parametrized by the angle θ between the bilinear and biquadratic couplings, using our previously developed label-free machine-learning pipeline \cite{Ming21, Ming23}, in which an autoencoder compresses iDMRG correlation matrices into latent features and K-means clustering with silhouette analysis groups them into phases. Without any prior labels, the scheme reveals four natural phases, the Haldane, dimerized, critical trimerized, and ferromagnetic phases, and accurately locates the Takhtajan-Babujian (θ=−π/4), SU(3)-symmetric Uimin-Lai-Sutherland (θ=π/4), and Ferro-Dimer (θ=5π/4) transition points; a shallow supervised refinement further sharpens the phase boundaries. We find that different correlation functions play fundamentally different roles in phase identification. The SU(3)-symmetric correlation alone selects a four-cluster partition that can be associated with the four phases, whereas the spin-spin and dimer-dimer correlations must be combined to recover the complete phase diagram. In contrast, the trimer-trimer correlation behaves as a marginal correlation: it is insufficient for phase identification by itself but improves the precision of the predicted phase boundaries when combined with the other correlations. Furthermore, the latent representations and silhouette values reveal signatures that we relate to the θ dependence of the correlations within each phase and, tentatively, to the underlying renormalization-group structure. These results demonstrate that machine learning can not only identify quantum phases but also uncover the physical information encoded in different correlation functions.
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BibTeX
@misc{chung2026deep,
title = {{Deep Learning of Phase Transitions for Bilinear-Biquadratic Spin-1 Chains from Correlation Aspects}},
author = {Ming-Chiang Chung and Yu-Ting Kuo and Ian McCulloch and Yuan-Hong Tsai},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.19995},
eprint = {2610.19995},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.19995},
}
RIS
TY - UNPB
TI - Deep Learning of Phase Transitions for Bilinear-Biquadratic Spin-1 Chains from Correlation Aspects
AU - Ming-Chiang Chung
AU - Yu-Ting Kuo
AU - Ian McCulloch
AU - Yuan-Hong Tsai
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.19995
UR - https://arxiv.org/abs/2610.19995
AB - We investigate the role of different correlation functions in reconstructing the phase diagram of the spin-1 bilinear-biquadratic chain, parametrized by the angle θ between the bilinear and biquadratic couplings, using our previously developed label-free machine-learning pipeline \cite{Ming21, Ming23}, in which an autoencoder compresses iDMRG correlation matrices into latent features and K-means clustering with silhouette analysis groups them into phases. Without any prior labels, the scheme reveals four natural phases, the Haldane, dimerized, critical trimerized, and ferromagnetic phases, and accurately locates the Takhtajan-Babujian (θ=−π/4), SU(3)-symmetric Uimin-Lai-Sutherland (θ=π/4), and Ferro-Dimer (θ=5π/4) transition points; a shallow supervised refinement further sharpens the phase boundaries. We find that different correlation functions play fundamentally different roles in phase identification. The SU(3)-symmetric correlation alone selects a four-cluster partition that can be associated with the four phases, whereas the spin-spin and dimer-dimer correlations must be combined to recover the complete phase diagram. In contrast, the trimer-trimer correlation behaves as a marginal correlation: it is insufficient for phase identification by itself but improves the precision of the predicted phase boundaries when combined with the other correlations. Furthermore, the latent representations and silhouette values reveal signatures that we relate to the θ dependence of the correlations within each phase and, tentatively, to the underlying renormalization-group structure. These results demonstrate that machine learning can not only identify quantum phases but also uncover the physical information encoded in different correlation functions.
ER -