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Mapping distinct phase transitions to a neural network / Dimitrios Bachtis; Gert Aarts; Biagio Lucini

Physical Review E, Volume: 102, Issue: 5

Swansea University Authors: Dimitrios, Bachtis, Gert, Aarts, Biagio, Lucini

Abstract

We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symmetry-breaking phase transitions in considered systems irrespective of the universality class, order, and the presence of...

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Published in: Physical Review E
ISSN: 2470-0045 2470-0053
Published: American Physical Society (APS) 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa55679
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Abstract: We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symmetry-breaking phase transitions in considered systems irrespective of the universality class, order, and the presence of discrete or continuous degrees of freedom. No prior knowledge about the existence of a phase transition is required in the target system and its entire parameter space can be scanned with multiple histogram reweighting to discover one. We establish our approach in q-state Potts models and perform a calculation for the critical coupling and the critical exponents of the φ4 scalar field theory using quantities derived from the neural network implementation. We view the machine learning algorithm as a mapping that associates each configuration across different systems to its corresponding phase and elaborate on implications for the discovery of unknown phase transitions.
College: College of Science
Issue: 5