{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unveiling-phase-transitions-with-machine","title":"Unveiling phase transitions with machine learning","arxiv_id":"1904.01486","date":"2019-04-02","proceeding":null,"authors":["Askery Canabarro","Felipe Fernandes Fanchini","André Luiz Malvezzi","Rodrigo Pereira","Rafael Chaves"],"abstract":"The classification of phase transitions is a central and challenging task in\ncondensed matter physics. Typically, it relies on the identification of order\nparameters and the analysis of singularities in the free energy and its\nderivatives. Here, we propose an alternative framework to identify quantum\nphase transitions, employing both unsupervised and supervised machine learning\ntechniques. Using the axial next-nearest neighbor Ising (ANNNI) model as a\nbenchmark, we show how unsupervised learning can detect three phases\n(ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating\nphase) as well as two distinct regions within the paramagnetic phase. Employing\nsupervised learning we show that transfer learning becomes possible: a machine\ntrained only with nearest-neighbour interactions can learn to identify a new\ntype of phase occurring when next-nearest-neighbour interactions are\nintroduced. All our results rely on few and low dimensional input data (up to\ntwelve lattice sites), thus providing a computational friendly and general\nframework for the study of phase transitions in many-body systems.","url_abs":"http://arxiv.org/abs/1904.01486v1","url_pdf":"http://arxiv.org/pdf/1904.01486v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unveiling-phase-transitions-with-machine","repo_url":"https://github.com/Marcus-Ambiel/ic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.01486","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}