Learning by confusion: The phase diagram of the Holstein model

Authors

George Issa, University of California, Davis
Owen Bradley, University of California, Davis
Ehsan Khatami, San Jose State UniversityFollow
Richard Scalettar, University of California, Davis

Publication Date

4-15-2025

Document Type

Article

Publication Title

Physical Review B

Volume

111

Issue

15

DOI

10.1103/PhysRevB.111.155140

Abstract

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.

Funding Sponsor

U.S. Department of Energy

Department

Physics and Astronomy

Recommended Citation

George Issa, Owen Bradley, Ehsan Khatami, and Richard Scalettar. "Learning by confusion: The phase diagram of the Holstein model" Physical Review B (2025). https://doi.org/10.1103/PhysRevB.111.155140