Learning by Confusion: The Phase Diagram of the Holstein Model
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910917610962944 |
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| author | Issa, George Bradley, Owen Khatami, Ehsan Scalettar, Richard |
| author_facet | Issa, George Bradley, Owen Khatami, Ehsan Scalettar, Richard |
| contents | We employ the "learning by confusion" technique, an unsupervised machine learning approach for detecting phase transitions, to analyze quantum Monte Carlo simulations of the two-dimensional Holstein model--a fundamental model for electron-phonon interactions on a lattice. Utilizing a convolutional neural network, we conduct a series of binary classification tasks to identify Holstein critical points based on the neural network's learning accuracy. We further evaluate the effectiveness of various training datasets, including snapshots of phonon fields and other measurements resolved in imaginary time, for predicting distinct phase transitions and crossovers. Our results culminate in the construction of the finite-temperature phase diagram of the Holstein model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning by Confusion: The Phase Diagram of the Holstein Model Issa, George Bradley, Owen Khatami, Ehsan Scalettar, Richard Strongly Correlated Electrons Disordered Systems and Neural Networks We employ the "learning by confusion" technique, an unsupervised machine learning approach for detecting phase transitions, to analyze quantum Monte Carlo simulations of the two-dimensional Holstein model--a fundamental model for electron-phonon interactions on a lattice. Utilizing a convolutional neural network, we conduct a series of binary classification tasks to identify Holstein critical points based on the neural network's learning accuracy. We further evaluate the effectiveness of various training datasets, including snapshots of phonon fields and other measurements resolved in imaginary time, for predicting distinct phase transitions and crossovers. Our results culminate in the construction of the finite-temperature phase diagram of the Holstein model. |
| title | Learning by Confusion: The Phase Diagram of the Holstein Model |
| topic | Strongly Correlated Electrons Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2501.04681 |