Djenabou Bayo, Burak Çivitcioğlu, Joseph J Webb, Andreas Honecker, Rudolf A. Römer
{"title":"物理学模型系统相位和结构的机器学习","authors":"Djenabou Bayo, Burak Çivitcioğlu, Joseph J Webb, Andreas Honecker, Rudolf A. Römer","doi":"arxiv-2409.03023","DOIUrl":null,"url":null,"abstract":"The detection of phase transitions is a fundamental challenge in condensed\nmatter physics, traditionally addressed through analytical methods and direct\nnumerical simulations. In recent years, machine learning techniques have\nemerged as powerful tools to complement these standard approaches, offering\nvaluable insights into phase and structure determination. Additionally, they\nhave been shown to enhance the application of traditional methods. In this\nwork, we review recent advancements in this area, with a focus on our\ncontributions to phase and structure determination using supervised and\nunsupervised learning methods in several systems: (a) 2D site percolation, (b)\nthe 3D Anderson model of localization, (c) the 2D $J_1$-$J_2$ Ising model, and\n(d) the prediction of large-angle convergent beam electron diffraction\npatterns.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":"5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning of phases and structures for model systems in physics\",\"authors\":\"Djenabou Bayo, Burak Çivitcioğlu, Joseph J Webb, Andreas Honecker, Rudolf A. Römer\",\"doi\":\"arxiv-2409.03023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The detection of phase transitions is a fundamental challenge in condensed\\nmatter physics, traditionally addressed through analytical methods and direct\\nnumerical simulations. In recent years, machine learning techniques have\\nemerged as powerful tools to complement these standard approaches, offering\\nvaluable insights into phase and structure determination. Additionally, they\\nhave been shown to enhance the application of traditional methods. In this\\nwork, we review recent advancements in this area, with a focus on our\\ncontributions to phase and structure determination using supervised and\\nunsupervised learning methods in several systems: (a) 2D site percolation, (b)\\nthe 3D Anderson model of localization, (c) the 2D $J_1$-$J_2$ Ising model, and\\n(d) the prediction of large-angle convergent beam electron diffraction\\npatterns.\",\"PeriodicalId\":501066,\"journal\":{\"name\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.03023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Disordered Systems and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.03023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning of phases and structures for model systems in physics
The detection of phase transitions is a fundamental challenge in condensed
matter physics, traditionally addressed through analytical methods and direct
numerical simulations. In recent years, machine learning techniques have
emerged as powerful tools to complement these standard approaches, offering
valuable insights into phase and structure determination. Additionally, they
have been shown to enhance the application of traditional methods. In this
work, we review recent advancements in this area, with a focus on our
contributions to phase and structure determination using supervised and
unsupervised learning methods in several systems: (a) 2D site percolation, (b)
the 3D Anderson model of localization, (c) the 2D $J_1$-$J_2$ Ising model, and
(d) the prediction of large-angle convergent beam electron diffraction
patterns.