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Mapping distinct phase transitions to a neural network

Dimitrios Bachtis, Gert Aarts Orcid Logo, Biagio Lucini Orcid Logo

Physical Review E, Volume: 102, Issue: 5

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

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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...

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Published in: Physical Review E
ISSN: 2470-0045 2470-0053
Published: American Physical Society (APS) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55679
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first_indexed 2020-11-17T08:00:18Z
last_indexed 2021-01-12T04:20:11Z
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spelling 2021-01-11T16:30:53.1808995 v2 55679 2020-11-17 Mapping distinct phase transitions to a neural network 91a311a58d3f8badc779f0ffa6d0ca3d Dimitrios Bachtis Dimitrios Bachtis true false 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 7e6fcfe060e07a351090e2a8aba363cf 0000-0001-8974-8266 Biagio Lucini Biagio Lucini true false 2020-11-17 SPH 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. Journal Article Physical Review E 102 5 American Physical Society (APS) 2470-0045 2470-0053 16 11 2020 2020-11-16 10.1103/physreve.102.053306 COLLEGE NANME Physics COLLEGE CODE SPH Swansea University European Research Council (ERC); UKRI STFC; Royal Society; Leverhulme Foundation; European Commission; ERDF 2021-01-11T16:30:53.1808995 2020-11-17T07:50:09.7606985 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics Dimitrios Bachtis 1 Gert Aarts 0000-0002-6038-3782 2 Biagio Lucini 0000-0001-8974-8266 3 55679__18675__cc0d8928a5e844e894f90d72dfb0af4d.pdf transferlearning.pdf 2020-11-17T07:56:29.9776882 Output 834636 application/pdf Accepted Manuscript true true eng https://arxiv.org/licenses/nonexclusive-distrib/1.0/license.html
title Mapping distinct phase transitions to a neural network
spellingShingle Mapping distinct phase transitions to a neural network
Dimitrios Bachtis
Gert Aarts
Biagio Lucini
title_short Mapping distinct phase transitions to a neural network
title_full Mapping distinct phase transitions to a neural network
title_fullStr Mapping distinct phase transitions to a neural network
title_full_unstemmed Mapping distinct phase transitions to a neural network
title_sort Mapping distinct phase transitions to a neural network
author_id_str_mv 91a311a58d3f8badc779f0ffa6d0ca3d
1ba0dad382dfe18348ec32fc65f3f3de
7e6fcfe060e07a351090e2a8aba363cf
author_id_fullname_str_mv 91a311a58d3f8badc779f0ffa6d0ca3d_***_Dimitrios Bachtis
1ba0dad382dfe18348ec32fc65f3f3de_***_Gert Aarts
7e6fcfe060e07a351090e2a8aba363cf_***_Biagio Lucini
author Dimitrios Bachtis
Gert Aarts
Biagio Lucini
author2 Dimitrios Bachtis
Gert Aarts
Biagio Lucini
format Journal article
container_title Physical Review E
container_volume 102
container_issue 5
publishDate 2020
institution Swansea University
issn 2470-0045
2470-0053
doi_str_mv 10.1103/physreve.102.053306
publisher American Physical Society (APS)
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
document_store_str 1
active_str 0
description 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.
published_date 2020-11-16T04:10:05Z
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score 11.013619

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