SciPost Submission Page
Rapid detection of phase transitions from Monte Carlo samples before equilibrium
by Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
This Submission thread is now published as
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: |
|
| 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)