[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127802-en":3,"doc-seo-127802-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127802,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning","The study presents a comprehensive theoretical analysis of a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable with van der Waals magnetic materials. A magnetic heterostructure model is constructed including up to second-neighbor interlayer interactions, revealing how geometric frustration and magnetic order interplay in this quasiperiodic system. An unsupervised machine-learning workflow maps the parameter space and distinguishes distinct magnetic phases. The work provides the magnetic phase diagram and identifies a unique collinear/non-collinear ferromagnetic alternating helical phase with logarithmically decreasing magnetization versus stack height.","arXiv :2307 . 16052v1 [ cond-mat .str-el ] 29 Jul 2023  \nUnveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning  \nPablo S. Cornaglia∗  \nCentro At´omico Bariloche and Instituto Balseiro, CNEA, 8400 Bariloche, Argentina Consejo Nacional de Investigaciones Cient´ıficas y T´ecnicas (CONICET), Argentina and Instituto de Nanociencia y Nanotecnolog´ıa CNEA-CONICET, Argentina  \nMatias Nu˜nez  \nConsejo Nacional de Investigaciones Cient´ıficas y T´ecnicas (CONICET), Argentina Instituto de Investigaciones en Biodiversidad y Medioambiente (INIBIOMA), Universidad Nacional del Comahue, Bariloche, Argentina and  \nUniversidad de Ingenieria y Tecnologia- UTEC, Lima, Per´u  \nD. J. Garcia  \nCentro At´omico Bariloche and Instituto Balseiro, CNEA, 8400 Bariloche, Argentina and Consejo Nacional de Investigaciones Cient´ıficas y T´ecnicas (CONICET), Argentina  \n(Dated: August 1, 2023)  \nIn this study, we conduct a comprehensive theoretical analysis of a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable using van der Waals magnetic materials. We construct a model of this magnetic heterostructure, which includes up to second neighbor interlayer magnetic interactions, that displays a complex relationship between geometric frustration and magnetic order in this quasicrystalline system. To navigate the parameter space and identify distinct magnetic phases, we employ a machine learning approach, which proves to be a powerful tool in revealing the complex magnetic behavior of this system. We offer a thorough description of the magnetic phase diagram as a function of the model parameters. Notably, we discover among other collinear and non-collinear phases, a unique ferromagnetic alternating helical phase. In this noncollinear quasiperiodic ferromagnetic configuration the magnetization decreases logarithmically with the stack height.  \nI. INTRODUCTION  \nThe advent of two-dimensional (2D) materials has opened up a new chapter in the field of condensed matter physics, offering a rich platform for exploring novel phenomena 1 . Among these, magnetic van der Waals (vdW) materials have attracted significant attention due to their unique magnetic properties and potential for integration into spintronic devices2–8 . These materials, characterized by their layered structure with weak interlayer bonding, offer the possibility of constructing heterostructures with tailored magnetic properties9 . For example, monolayer CrI3 has been reported to be ferromagnetic4 but the coupling between two layers can be ferromagnetic or antiferromagnetic depending on the type of stacking 10 . The stacking of magnetic layers in a single heterostructure provides an opportunity to engineer the magnetic properties at the atomic scale, potentially leading to the realization of novel magnetic phasesand spin textures.  \nOver the past few years, machine learning (ML) techniques have been increasingly utilized in condensed matter physics research due to their capabilities of dealing with large and complex data sets 11–15 . Particularly, they provide a means to identify patterns and correlations within the data, which would be otherwise challenging or impossible to identify manually. This has allowed for new insights into several areas, such as phase transitions 11,16 ,  \nmany-body localization 17 , topological materials 18 , and visualization of band structure spaces from electronic structure databases 13 . In the realm of 2D materials 19 and magnetic systems, ML techniques offer the possibility to help understand the magnetic properties of complex heterostructures.  \nIn this theoretical study, we explore the magnetism in heterostructures having a quasicrystalline stacking of ferromagnetic layers. Quasicrystals are aperiodic structures that display sharp peaks in Bragg diffraction but lack translational symmetry20 . These materials have been extensively studied in the context of electronic transp","cbCairbqZStdddLQ","https://ap.wps.com/l/cbCairbqZStdddLQ","pdf",4300330,1,11,"English","en",105,"# Introduction\n## Two-dimensional magnetic van der Waals materials\n## Machine learning in condensed matter physics\n## Quasicrystals and Fibonacci quasiperiodicity\n## Previous work on magnetic excitations and focus on ground states","[{\"question\":\"What system and interactions does the study model?\",\"answer\":\"It models a Fibonacci quasicrystalline stacking of ferromagnetic layers with interlayer magnetic interactions including up to second neighbors.\"},{\"question\":\"How is machine learning used to explore the magnetic behavior?\",\"answer\":\"An unsupervised machine-learning approach, combined with dimensionality reduction (PCA), is used to navigate the parameter space and identify distinct magnetic phases.\"},{\"question\":\"What notable magnetic phase is discovered and what is its key property?\",\"answer\":\"A unique ferromagnetic alternating helical phase is found among collinear and non-collinear phases, where the magnetization decreases logarithmically with stack height.\"}]","Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning | 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system and interactions does the study model?","Question",{"text":76,"@type":77},"It models a Fibonacci quasicrystalline stacking of ferromagnetic layers with interlayer magnetic interactions including up to second neighbors.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is machine learning used to explore the magnetic behavior?",{"text":81,"@type":77},"An unsupervised machine-learning approach, combined with dimensionality reduction (PCA), is used to navigate the parameter space and identify distinct magnetic phases.",{"name":83,"@type":74,"acceptedAnswer":84},"What notable magnetic phase is discovered and what is its key property?",{"text":85,"@type":77},"A unique ferromagnetic alternating helical phase is found among collinear and non-collinear phases, where the magnetization decreases logarithmically with stack 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