[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126274-en":3,"doc-seo-126274-105":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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126274,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Remote-sensing based control of 3D magnetic fields using machine learning for in operando applications","In operando techniques enable real-time measurement of intricate physical properties at micro- and nano-scale under external stimuli, supporting the study of diverse materials and functionalities. In nanomagnetism, achieving precise 3D magnetic field control is crucial for accessing complex magnetic states, yet direct measurement at the sample is often limited by constraints on source geometry and sensor placement. This work presents machine-learning-based calibration and control using a hexapole electromagnet and a multi-layer perceptron to model non-linear relations between remote and sample frames, delivering high accuracy and strong generalization for unseen field sequences, enabling robust in operando experimentation.","RESEARCH ARTICLE | MARCH 21 2025  \nRemote-sensing based control of 3D magnetic fields using machine learning for in operando applications  \nMiguel A. Cascales Sandoval 􀀤  ; J. Jurczyk  ; L. Skoric  ; D. Sanz-Hernández  ; N. Leo  ;  \nA. Kovacs  ; T. Schrefl  ; A. Hierro-Rodríguez  ; A. Fernández-Pacheco 􀀤   \nJ. Appl. Phys. 137, 113905 (2025)  \n[https://doi.org/10.1063/5.0249846](https://doi.org/10.1063/5.0249846)  \n􀀪  \nView Online  \n􀀮  \nExport Citation  \nArticles You May Be Interested In  \nOperando control of skyrmion density in a Lorentz transmission electron microscope with current pulses  \nJ. Appl. Phys. (December 2020)  \nDual reactor for in situ/operando fluorescent mode XAS studies of sample containing low-concentration 3dor 5d metal elements  \nRev. Sci. Instrum. (May 2018)  \nCharacterizing battery materials and electrodes via in situ/operando transmission electron microscopy  \nChem. Phys. Rev. (September 2022)  \n14 April 2025 08:22:17  \nRemote-sensing based control of 3D magnetic fields using machine learning for in operando applications  \n\n| Cite as: J. Appl. Phys. 137, 113905 (2025); doi: 10. 1063/5.0249846 Submitted: 20 November 2024 · Accepted: 24 February 2025 ·\u003Cbr>Published Online: 21 March 2025 | \u003Cbr>View Online | \u003Cbr>Export Citation | \u003Cbr>CrossMark |\n| --- | --- | --- | --- |\n| Miguel A. Cascales Sandoval,1,a)  J. Jurczyk,1  L. Skoric,2  D. Sanz-Hernández,3  N. Leo,1,4  A. Kovacs,5  T. Schrefl,5,6  A. Hierro-Rodríguez,7,8,9  and A. Fernández-Pacheco1,a)  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Institute of Applied Physics, TU Wien, Wiedner Hauptstraße 8-10, Vienna 1040, Austria\u003Cbr>2Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, United Kingdom\u003Cbr>3CNRS/Thales: Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 1 avenue Augustin Fresnel, 91767 Palaiseau, France 4 Department of Physics, Loughborough University, Epinal Way, Loughborough LE11 3TU, United Kingdom\u003Cbr>5 Department for Integrated Sensor Systems, Danube University Krems, Viktor Kaplan-Straße 2E, 2700 Wiener Neustadt, Austria 6Christian Doppler Laboratory for Magnet design through physics informed machine learning, Viktor Kaplan-Straße 2E,\u003Cbr>2700 Wiener Neustadt, Austria\u003Cbr>7 Departamento de Física, Universidad de Oviedo, Oviedo 33007, Spain\u003Cbr>8CINN (CSIC-Universidad de Oviedo), El Entrego 33940, Spain\u003Cbr>9SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, United Kingdom\u003Cbr>a)[Authors to whom correspondence should be addressed:](Authors to whom correspondence should be addressed: miguel.cascales@tuwien.ac.at and)[ miguel.cascales@tuwien.ac.at](Authors to whom correspondence should be addressed: miguel.cascales@tuwien.ac.at and)[ and](Authors to whom correspondence should be addressed: miguel.cascales@tuwien.ac.at and) [amalio.fernandez-pacheco@tuwien.ac.at](amalio.fernandez-pacheco@tuwien.ac.at) |  |  |  |\n| ABSTRACT\u003Cbr>In operando techniques enable real-time measurement of intricate physical properties at the micro- and nano-scale under external stimuli, allowing the study of a wide range of materials and functionalities. In nanomagnetism, in operando techniques greatly benefit from precise three-dimensional (3D) magnetic field control, enabling access to complex magnetic states forming in systems where multiple energies are set to compete with each other. However, achieving such precision is challenging and uncommon, as specific applications impose constraints on the type and geometry of magnetic field sources, limiting their capabilities. Here, we introduce an approach that leverages machine learning algorithms to achieve precise 3D magnetic field control using a hexapole electromagnet that is composed of three independent, non-collinear dipole electromagnets. In our experimental setup, magnetic field sensors are placed at a distance from the sample position due to inherent constraints, leading to indirect field measurements that differ from the magnetic field experienced by the sampl","cbCaijmpHQ2Cmg12","https://ap.wps.com/l/cbCaijmpHQ2Cmg12","pdf",2206386,6,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is precise 3D magnetic field control important in in operando nanomagnetism?\",\"answer\":\"It enables access to complex magnetic states by letting researchers study how competing energies shape the live behavior of nanomagnetic systems under external stimuli.\"},{\"question\":\"What challenge does the setup create for magnetic-field calibration?\",\"answer\":\"Sensors are placed away from the sample, so measurements in the remote frame differ from the magnetic field actually experienced by the sample.\"},{\"question\":\"How does the proposed method achieve accurate control?\",\"answer\":\"It uses a multi-layer perceptron neural network trained on inputs from a dynamic magnetic-field sequence to learn the non-linear remote-to-sample mapping, yielding high calibration accuracy and strong generalization to unseen sequences.\"}]","Remote-sensing based control of 3D magnetic fields using machine learning for in operando applications | 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is precise 3D magnetic field control important in in operando nanomagnetism?","Question",{"text":76,"@type":77},"It enables access to complex magnetic states by letting researchers study how competing energies shape the live behavior of nanomagnetic systems under external stimuli.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What challenge does the setup create for magnetic-field calibration?",{"text":81,"@type":77},"Sensors are placed away from the sample, so measurements in the remote frame differ from the magnetic field actually experienced by the sample.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed method achieve accurate control?",{"text":85,"@type":77},"It uses a multi-layer perceptron neural network trained on inputs from a dynamic magnetic-field sequence to learn the non-linear remote-to-sample mapping, yielding high calibration accuracy and strong generalization to unseen 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