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Large-scale parameter space exploration produces many thousands of configurations that are typically classified manually. An unsupervised workflow is presented using a convolutional auto-encoder combined with DBSCAN clustering to automate sighting and labeling. Tests use micromagnetic simulations to generate FeGe disc phase diagrams under varying external fields, achieving accurate and fast classification with minimal human input and strong agreement with published results.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-classification-of-vector-field-configurations-research-article/290758/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-classification-of-vector-field-configurations-research-article/290758.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"How is the proposed classification method evaluated?","Question",{"text":112,"@type":113},"The method is tested by generating magnetization configurations via computer simulation for a FeGe disc across changing external magnetic fields, then building the magnetic phase diagram and comparing classification quality against published results.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},290758,1789642515,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":121,"read_time":135,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","RESEARCH ARTICLE | FEBRUARY 02 2024  \nMachine learning based classification of vector field configurations  \nSpecial Collection: 68th Annual Conference on Magnetism and Magnetic Materials  \nSwapneel Amit Pathak 􀀧  ; Kurt Rahir  ; Sam Holt  ; Martin Lang  ; Hans Fangohr   \nAIP Advances 14, 025004 (2024)  \n[https://doi.org/10.1063/9.0000686](https://doi.org/10.1063/9.0000686)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nCrossMark  \n12 February 2024 10:54:35  \nAIP Advances ARTICLE  \n[pubs.aip.org/aip/adv](pubs.aip.org/aip/adv)  \nMachine learning based classification of vector field configurations  \n\n| Cite as: AIP Advances 14, 025004 (2024); doi: 10. 1063/9.0000686 Submitted: 1 October 2023 • Accepted: 22 November 2023 •\u003Cbr>Published Online: 2 February 2024 |  | | | |\n| --- | --- | --- | --- | --- |\n| Swapneel Amit Pathak,1 , 2, a)  Kurt Rahir,3  Sam Holt,1 , 2  Martin Lang,1 , 2 , 3  and Hans Fangohr1 , 2 , 3  |  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Max-Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany\u003Cbr>2 Center for Free-Electron Laser Science, Luruper Chaussee 149, 22761 Hamburg, Germany\u003Cbr>3 University of Southampton, Southampton SO17 1BJ, United Kingdom\u003Cbr>Note: This paper was presented at the 68th Annual Conference on Magnetism and Magnetic Materials.\u003Cbr>a)Author to whom correspondence should be addressed: [swapneel-amit.pathak@mpsd.mpg.de](swapneel-amit.pathak@mpsd.mpg.de) |  |  |  |  |\n| ABSTRACT\u003Cbr>Magnetic materials at the nanoscale are important for science and technology. A key aspect for their research and advancement is the understanding of the emerging magnetization vector field configurations within samples and devices. A systematic parameter space exploration—varying for example material parameters, temperature, or sample geometry—leads to the creation of many thousands of field configurations that need to be sighted and classified. This task is usually carried out manually, for example by looking at a visual representation of the field configurations. We report that it is possible to automate this process using an unsupervised machine learning algorithm, greatly reducing the human effort. We use a combination of convolutional auto-encoder and density-based spatial clustering of applications with noise (DBSCAN) algorithm. To evaluate the method, we create the magnetic phase diagram of a FeGe disc as a function of changing external magnetic field using computer simulation to generate the configurations. We find that the classification algorithm is accurate, fast, requires little human intervention, and compares well against the published results in the literature on the same material geometry and range of external fields. Our study shows that machine learning can be a powerful tool in the research of magnetic materials by automating the classification of magnetization field configurations.\u003Cbr>© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). [https://doi.org/10.1063/9.0000686](https://doi.org/10.1063/9.0000686) |  |  |  |  |\n| I. INTRODUCTION\u003Cbr>Magnetic materials play important roles in domains ranging from geology to data storage and medicine. Computer simulation have enabled research advances in materials research in fundamental science and virtual design optimization in industry. A particular strength of the simulation based parameter space exploration is the ability to quickly generate many possible designs or magnetization configurations. The analysis and evaluation of such large datasets typically requires considerable human effort. In this work, we explore the suitability of Machine Learning (ML) to provide automatic classification of large sets of vector field configurations, in the context of vector fields describing magnetization fields.\u003Cbr>ML is the branch of Artificial Intelligence","cbCaitZOUL2ETQ8A","https://ap.wps.com/l/cbCaitZOUL2ETQ8A","pdf",7876918,"English","# Abstract\n# I. Introduction\n## Background and motivation\n## Prior work using Ising model and PCA\n## Proposed ML approach and evaluation setup","[{\"question\":\"How is the proposed classification method evaluated?\",\"answer\":\"The method is tested by generating magnetization configurations via computer simulation for a FeGe disc across changing external magnetic fields, then building the magnetic phase diagram and comparing classification quality against published results.\"}]","Machine learning based classification of vector field configurations - Research article | PDF",15]