Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911552040337408 |
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| author | Abuali, Ahmed Clarke, David A. Hjorth-Jensen, Morten Konstantinidis, Ioannis Ratti, Claudia Yang, Jianyi |
| author_facet | Abuali, Ahmed Clarke, David A. Hjorth-Jensen, Morten Konstantinidis, Ioannis Ratti, Claudia Yang, Jianyi |
| contents | We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the 3D Ising model. A 3D convolutional neural network 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 $T_c=4.5128(58)$ and the correlation-length critical exponent $ν=0.63(27)$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder Abuali, Ahmed Clarke, David A. Hjorth-Jensen, Morten Konstantinidis, Ioannis Ratti, Claudia Yang, Jianyi Statistical Mechanics High Energy Physics - Lattice Nuclear Theory Data Analysis, Statistics and Probability We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the 3D Ising model. A 3D convolutional neural network 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 $T_c=4.5128(58)$ and the correlation-length critical exponent $ν=0.63(27)$, 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. |
| title | Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder |
| topic | Statistical Mechanics High Energy Physics - Lattice Nuclear Theory Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.20157 |