{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:29:02Z","timestamp":1779928142331,"version":"3.53.1"},"reference-count":42,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":19,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Korea government","award":["2022R1F1A1074054"],"award-info":[{"award-number":["2022R1F1A1074054"]}]},{"DOI":"10.13039\/501100010446","name":"Institute for Basic Science","doi-asserted-by":"crossref","award":["IBS-R024-D1"],"award-info":[{"award-number":["IBS-R024-D1"]}],"id":[{"id":"10.13039\/501100010446","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI<jats:sub>3<\/jats:sub> from its magnetic domain images generated through atomistic spin simulations. The \u2018generative model\u2019 excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad56fa","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T22:46:51Z","timestamp":1718146011000},"page":"025073","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9381-1253","authenticated-orcid":true,"given":"Woo Seok","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taegeun","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2468-3152","authenticated-orcid":true,"given":"Kyoung-Min","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"mlstad56fabib1","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1126\/science.abj7478","article-title":"Direct visualization of magnetic domains and moir\u00e9 magnetism in twisted 2D magnets","volume":"374","author":"Song","year":"2021","journal-title":"Science"},{"key":"mlstad56fabib2","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1038\/s41565-021-01014-y","article-title":"Coexisting ferromagnetic\u2013antiferromagnetic state in twisted bilayer CrI3","volume":"17","author":"Xu","year":"2022","journal-title":"Nat. Nanotechnol."},{"key":"mlstad56fabib3","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1038\/s41567-021-01408-8","article-title":"Twist engineering of the two-dimensional magnetism in double bilayer chromium triiodide homostructures","volume":"18","author":"Xie","year":"2022","journal-title":"Nat. Phys."},{"key":"mlstad56fabib4","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1038\/s41567-023-02061-z","article-title":"Evidence of non-collinear spin texture in magnetic Moir\u00e9 superlattices","volume":"19","author":"Xie","year":"2023","journal-title":"Nat. Phys."},{"key":"mlstad56fabib5","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1038\/s41928-023-00978-0","article-title":"Electrically tunable moir\u00e9 magnetism in twisted double bilayers of chromium triiodide","volume":"6","author":"Cheng","year":"2023","journal-title":"Nat. Electron."},{"key":"mlstad56fabib6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2000347117","article-title":"Noncollinear phases in moir\u00e9 magnets","volume":"117","author":"Hejazi","year":"2020","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"mlstad56fabib7","doi-asserted-by":"publisher","first-page":"6633","DOI":"10.1021\/acs.nanolett.1c02096","article-title":"Moir\u00e9 skyrmions and chiral magnetic phases in twisted CrX3 (X = I, Br and Cl) bilayers","volume":"21","author":"Akram","year":"2021","journal-title":"Nano Lett."},{"key":"mlstad56fabib8","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202206923","article-title":"Magnetic skyrmion lattices in a novel 2D-twisted bilayer magnet","volume":"33","author":"Zheng","year":"2023","journal-title":"Adv. Funct. Mater."},{"key":"mlstad56fabib9","doi-asserted-by":"publisher","first-page":"6088","DOI":"10.1021\/acs.nanolett.3c01529","article-title":"Ab initio spin Hamiltonian and topological noncentrosymmetric magnetism in twisted bilayer CrI3","volume":"23","author":"Kim","year":"2023","journal-title":"Nano Lett."},{"key":"mlstad56fabib10","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1038\/s43588-023-00430-5","article-title":"Moir\u00e9 magnetic exchange interactions in twisted magnets","volume":"3","author":"Yang","year":"2023","journal-title":"Nat. Comput. Sci."},{"key":"mlstad56fabib11","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.104.L100406","article-title":"Heterobilayer Moir\u00e9 magnets: Moir\u00e9 skyrmions and commensurate-incommensurate transitions","volume":"104","author":"Hejazi","year":"2021","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.108.174440","article-title":"Skyrmion dynamics in Moir\u00e9 magnets","volume":"108","author":"Shaban","year":"2023","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib13","doi-asserted-by":"publisher","first-page":"7194","DOI":"10.1021\/acs.nanolett.8b03315","article-title":"Skyrmions in the moir\u00e9 of van der Waals 2D magnets","volume":"18","author":"Tong","year":"2018","journal-title":"Nano Lett."},{"key":"mlstad56fabib14","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.103.L140406","article-title":"Skyrmions in twisted van der Waals magnets","volume":"103","author":"Akram","year":"2021","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib15","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.104.014410","article-title":"Hierarchy of multi-order skyrmion phases in twisted magnetic bilayers","volume":"104","author":"Ray","year":"2021","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib16","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.3.013027","article-title":"Magnetization textures in twisted bilayer CrX3 (X = Br, I)","volume":"3","author":"Xiao","year":"2021","journal-title":"Phys. Rev. Res."},{"key":"mlstad56fabib17","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s42005-022-00972-6","article-title":"Whirling interlayer fields as a source of stable topological order in moir\u00e9 CrI3","volume":"5","author":"Ghader","year":"2022","journal-title":"Commun. Phys."},{"key":"mlstad56fabib18","doi-asserted-by":"publisher","DOI":"10.1088\/2053-1583\/acc671","article-title":"Moir\u00e9-driven multiferroic order in twisted CrCl3, CrBr3 and CrI3 bilayers","volume":"10","author":"Fumega","year":"2023","journal-title":"2D Mater."},{"key":"mlstad56fabib19","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.108.L100401","article-title":"Controllable magnetic domains in twisted trilayer magnets","volume":"108","author":"Kim","year":"2023","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib20","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1021\/acs.nanolett.3c03246","article-title":"Emergence of stable meron quartets in twisted magnets","volume":"24","author":"Kim","year":"2024","journal-title":"Nano Lett."},{"key":"mlstad56fabib21","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1021\/acs.nanolett.3c04084","article-title":"Theory of Moir\u00e9 magnetism in twisted bilayer \u03b1-RuCl3","volume":"24","author":"Akram","year":"2024","journal-title":"Nano Lett."},{"key":"mlstad56fabib22","doi-asserted-by":"publisher","first-page":"eabb0872","DOI":"10.1126\/sciadv.abb0872","article-title":"Magnetic Hamiltonian parameter estimation using deep learning techniques","volume":"6","author":"Kwon","year":"2020","journal-title":"Sci. Adv."},{"key":"mlstad56fabib23","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1038\/s41586-021-04223-6","article-title":"Deep physical neural networks trained with backpropagation","volume":"601","author":"Wright","year":"2022","journal-title":"Nature"},{"key":"mlstad56fabib24","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"mlstad56fabib25","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.2.033429","article-title":"Ab initio solution of the many-electron Schr\u00f6dinger equation with deep neural networks","volume":"2","author":"Pfau","year":"2020","journal-title":"Phys. Rev. Res."},{"key":"mlstad56fabib26","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1002\/prot.25834","article-title":"Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)","volume":"87","author":"Senior","year":"2019","journal-title":"Proteins Struct. Funct. Bioinf."},{"key":"mlstad56fabib27","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1126\/science.aag2302","article-title":"Solving the quantum many-body problem with artificial neural networks","volume":"355","author":"Carleo","year":"2017","journal-title":"Science"},{"key":"mlstad56fabib28","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.97.035116","article-title":"Approximating quantum many-body wave functions using artificial neural networks","volume":"97","author":"Cai","year":"2018","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.99.174426","article-title":"Application of machine learning to two-dimensional Dzyaloshinskii-Moriya ferromagnets","volume":"99","author":"Singh","year":"2019","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.99.024423","article-title":"Searching magnetic states using an unsupervised machine learning algorithm with the heisenberg model","volume":"99","author":"Kwon","year":"2019","journal-title":"Phys. Rev. B"},{"key":"mlstad56fabib31","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3938\/jkps.76.118","article-title":"Heterogeneous trp channel model of a chordotonal neuron might explain drosophila hearing","volume":"76","author":"Lee","year":"2020","journal-title":"J. Korean Phys. Soc."},{"key":"mlstad56fabib32","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s40042-021-00056-8","article-title":"Machine learning for the diagnosis of early-stage diabetes using temporal glucose profiles","volume":"78","author":"Lee","year":"2021","journal-title":"J. Korean Phys. Soc."},{"key":"mlstad56fabib33","doi-asserted-by":"publisher","DOI":"10.1088\/1748-3190\/abc869","article-title":"Fast frequency discrimination and phoneme recognition using a biomimetic membrane coupled to a neural network","volume":"16","author":"Lee","year":"2021","journal-title":"Bioinspir. Biomim."},{"key":"mlstad56fabib34","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abb6d3","article-title":"Deep learning of chaos classification","volume":"1","author":"Lee","year":"2020","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad56fabib35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2023.102053","article-title":"Estimation of correlation matrices from limited time series data using machine learning","volume":"71","author":"Easaw","year":"2023","journal-title":"J. Comput. Sci"},{"key":"mlstad56fabib36","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/acffa2","article-title":"Equivariant neural networks for spin dynamics simulations of itinerant magnets","volume":"4","author":"Miyazaki","year":"2023","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstad56fabib37","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.041028","article-title":"Topological spin excitations in honeycomb ferromagnet CrI3","volume":"8","author":"Chen","year":"2018","journal-title":"Phys. Rev. X"},{"key":"mlstad56fabib38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput."},{"key":"mlstad56fabib39","article-title":"Gaussian error linear units (GELUs)","author":"Hendrycks","year":"2023"},{"key":"mlstad56fabib40","author":"Jolliffe","year":"1986"},{"key":"mlstad56fabib41","author":"Shalev-Shwartz","year":"2014"},{"key":"mlstad56fabib42","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2014\/09\/P09008","article-title":"Magnetization dynamics: path-integral formalism for the stochastic Landau-Lifshitz-Gilbert equation","author":"Aron","year":"2014","journal-title":"J. Stat. Mech."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T09:58:02Z","timestamp":1718877482000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad56fa"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,1]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6,20]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad56fa","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,1]]},"assertion":[{"value":"Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-02-22","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-06-11","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-06-20","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}