Machine learning of phase transitions in nonlinear polariton lattices
dc.contributor.author | Zvyagintseva, D | |
dc.contributor.author | Sigurdsson, H | |
dc.contributor.author | Kozin, VK | |
dc.contributor.author | Iorsh, I | |
dc.contributor.author | Shelykh, IA | |
dc.contributor.author | Ulyantsev, V | |
dc.contributor.author | Kyriienko, O | |
dc.date.accessioned | 2022-01-26T14:30:24Z | |
dc.date.issued | 2022-01-10 | |
dc.date.updated | 2022-01-26T13:38:06Z | |
dc.description.abstract | Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudospin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Russian Foundation for Basic Research | en_GB |
dc.description.sponsorship | NATO | en_GB |
dc.description.sponsorship | Icelandic Research Fund | en_GB |
dc.description.sponsorship | Icelandic Research Fund | en_GB |
dc.format.extent | 8- | |
dc.identifier.citation | Vol. 5, article 8 | en_GB |
dc.identifier.doi | https://doi.org/10.1038/s42005-021-00755-5 | |
dc.identifier.grantnumber | EP/V00171X/1 | en_GB |
dc.identifier.grantnumber | 21-52-12038 | en_GB |
dc.identifier.grantnumber | NATO.SPS.MYP.G5860 | en_GB |
dc.identifier.grantnumber | 217631-051 | en_GB |
dc.identifier.grantnumber | 163082-051 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/128576 | |
dc.identifier | ORCID: 0000-0002-6259-6570 (Kyriienko, Oleksandr) | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.rights | © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | en_GB |
dc.title | Machine learning of phase transitions in nonlinear polariton lattices | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-01-26T14:30:24Z | |
dc.identifier.issn | 2399-3650 | |
exeter.article-number | 8 | |
dc.description | This is the final version. Available from Nature Research via the DOI in this record. | en_GB |
dc.description | The data that support the findings of this study are available from the corresponding author upon reasonable request. | en_GB |
dc.description | The code for the analysis is available from the corresponding author upon reasonable request. | en_GB |
dc.identifier.eissn | 2399-3650 | |
dc.identifier.journal | Communications Physics | en_GB |
dc.relation.ispartof | Communications Physics, 5(1) | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2021-11-03 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-11-03 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-01-26T14:21:52Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-01-26T14:30:31Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2022-01-10 |
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