{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:57:17Z","timestamp":1769759837042,"version":"3.49.0"},"reference-count":68,"publisher":"IOP Publishing","issue":"41","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Phys.: Condens. Matter"],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The Berezinskii\u2013Kosterlitz\u2013Thouless (BKT) transition in magnetic systems is an intriguing phenomenon, and estimating the BKT transition temperature is a long-standing problem. In this work, we explore anisotropic classical Heisenberg XY and XXZ models with ferromagnetic exchange on a square lattice and antiferromagnetic exchange on a triangular lattice using an unsupervised machine learning approach called principal component analysis (PCA). The earlier PCA studies of the BKT transition temperature (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:msub>\n                              <mml:mi>T<\/mml:mi>\n                              <mml:mrow>\n                                 <mml:mi>BKT<\/mml:mi>\n                              <\/mml:mrow>\n                           <\/mml:msub>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  \n                  <\/jats:inline-formula>) using the vorticities as input fail to give any conclusive results, whereas, in this work, we show that the proper analysis of the first principal component-temperature curve can estimate <jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:msub>\n                              <mml:mi>T<\/mml:mi>\n                              <mml:mrow>\n                                 <mml:mi>BKT<\/mml:mi>\n                              <\/mml:mrow>\n                           <\/mml:msub>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  \n                  <\/jats:inline-formula> which is consistent with the existing literature. This analysis works well for the anisotropic classical Heisenberg with a ferromagnetic exchange on a square lattice and for frustrated antiferromagnetic exchange on a triangular lattice. The classical anisotropic Heisenberg antiferromagnetic model on the triangular lattice has two close transitions: the <jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:msub>\n                              <mml:mi>T<\/mml:mi>\n                              <mml:mrow>\n                                 <mml:mi>BKT<\/mml:mi>\n                              <\/mml:mrow>\n                           <\/mml:msub>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  \n                  <\/jats:inline-formula> and Ising-like phase transition for chirality at <jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:msub>\n                              <mml:mi>T<\/mml:mi>\n                              <mml:mrow>\n                                 <mml:mi mathvariant=\"normal\">c<\/mml:mi>\n                              <\/mml:mrow>\n                           <\/mml:msub>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  \n                  <\/jats:inline-formula>, and it is difficult to separate these transition points. It is also noted that using the PCA method and manipulation of their first principal component not only makes the separation of transition points possible but also determines transition temperature.<\/jats:p>","DOI":"10.1088\/1361-648x\/ad5d35","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T18:29:03Z","timestamp":1719599343000},"page":"415804","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Study of the Berezinskii\u2013Kosterlitz\u2013Thouless transition: an unsupervised machine learning approach"],"prefix":"10.1088","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5708-8360","authenticated-orcid":true,"given":"Sumit","family":"Haldar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1248-9539","authenticated-orcid":true,"given":"Sk Saniur","family":"Rahaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoranjan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"cmad5d35bib1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"cmad5d35bib2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"cmad5d35bib3","first-page":"p 1097","volume":"vol 25","author":"Krizhevsky","year":"2012"},{"key":"cmad5d35bib4","first-page":"pp 430","author":"Rosten","year":"2006"},{"key":"cmad5d35bib5","first-page":"p 8697","author":"Zoph","year":"2018"},{"key":"cmad5d35bib6","first-page":"1","volume":"128","author":"Bishop","year":"2006","journal-title":"Mach. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-12-19","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-06-28","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-07-16","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}