This content will become publicly available on July 1, 2027

Title: Detecting the three-dimensional Ising model phase transition with a ground-state-trained autoencoder
We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the three-dimensional (3D) Ising model. A 3D convolutional autoencoder (CAE) is trained on ground-state configurations only, without prior knowledge of the critical temperature, the Hamiltonian, or the order parameter. After training, the model is applied to Monte Carlo configurations across a wide temperature range and different lattice sizes. The mean-square reconstruction error is shown to be sensitive to the transition. Finite-size scaling of the peak location for the reconstruction error susceptibility yields the critical temperature Tc=4.5128(58) , in excellent agreement with the known value. Additionally, we obtain an estimate of the correlation-length critical exponent, ν=0.63(27) , also consistent with results from the literature. Our results show that a one-class CAE, trained on zero-temperature configurations only, can recover nontrivial critical behavior of the 3D Ising model.  more » « less
Award ID(s):
2621752 2514763 2116686 2103680
PAR ID:
10704539
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
APS
Date Published:
Journal Name:
APS Open Science
Volume:
1
ISSN:
3070-2240
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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