Journal article 729 views 247 downloads
Mapping distinct phase transitions to a neural network
Dimitrios Bachtis,
Gert Aarts 
,
Biagio Lucini
,
Biagio Lucini
Physical Review E, Volume: 102, Issue: 5
Swansea University Authors:
Dimitrios Bachtis, Gert Aarts , Biagio Lucini
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DOI (Published version): 10.1103/physreve.102.053306
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...
| Published in: | Physical Review E |
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| ISSN: | 2470-0045 2470-0053 |
| Published: |
American Physical Society (APS)
2020
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| Online Access: |
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| 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. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
European Research Council (ERC); UKRI STFC; Royal Society; Leverhulme Foundation; European Commission; ERDF |
| Issue: |
5 |