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Rapid detection of phase transitions from Monte Carlo samples before equilibrium

by Jiewei Ding, Ho-Kin Tang, Wing Chi Yu

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Submission summary

Authors (as registered SciPost users): Wing Chi Yu
Submission information
Preprint Link: scipost_202203_00027v3  (pdf)
Code repository: https://github.com/ParcoDing/Rapid-detection
Data repository: https://github.com/ParcoDing/Rapid-detection
Date accepted: 2022-08-02
Date submitted: 2022-07-28 08:58
Submitted by: Yu, Wing Chi
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
Specialties:
  • Condensed Matter Physics - Computational
Approach: Computational

Abstract

We found that Bidirectional LSTM and Transformer can classify different phases of condensed matter models and determine the phase transition points by learning features in the Monte Carlo raw data before equilibrium. Our method can significantly reduce the time and computational resources required for probing phase transitions as compared to the conventional Monte Carlo simulation. We also provide evidence that the method is robust and the performance of the deep learning model is insensitive to the type of input data (we tested spin configurations of classical models and green functions of a quantum model), and it also performs well in detecting Kosterlitz–Thouless phase transitions.

List of changes

1. Corrected spelling and grammatical mistakes.
2. Rephrased the last sentence in the first paragraph of Section 2.

Published as SciPost Phys. 13, 057 (2022)

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