[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118814-en":3,"doc-seo-118814-105":29,"detail-sidebar-cat-0-en-105":91},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118814,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Classification of magnetic order from electronic structure by using machine learning","Identifying magnetic order in materials is essential for designing functional compounds, yet direct determination is hindered by limitations of neutron scattering experiments, including facility accessibility, sample size requirements, and large neutron absorption. This work proposes a decision-tree machine-learning framework that classifies antiferromagnetic orders using spin-integrated excitation spectra derived from Hartree-Fock mean-field calculations on a Wannier Hamiltonian. Training uses multiple spectral inputs such as density of states and momentum-resolved spectra at high-symmetry points. Results show that density of states performs well, while spectral broadening strongly affects accuracy; incorporating excitation energy as a feature improves generalization to differently generated test samples.","arXiv :2302 . 13329v2 [ cond-mat .mtrl-sci ] 23 Aug 2023  \nClassification of magnetic order from electronic structure by using machine learning  \nYerin Jang, 1 Choong H. Kim,2, 3, ∗ and Ara Go 1,†  \n1 Department of Physics, Chonnam National University, Gwangju 61186, Korea  \n2 Center for Correlated Electron Systems, Institute for Basic Science, Seoul 08826, Korea  \n3 Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea (Dated: August 24, 2023)  \nIdentifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO3 . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model’s performance. We improved the model’s performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.  \nI. INTRODUCTION  \nMagnetism plays a crucial role in many physical and technological phenomena, ranging from magnetic storage devices to superconductivity. Determining the presence of long-range magnetic ordering in materials is therefore essential for designing new functional materials with tailored magnetic properties. Neutron scattering is a powerful tool for directly determining magnetic order and is functional across a wide range of temperatures and pressures. However, neutron scattering experiments typically require access to specialized facilities, such as nuclear reactors or spallation sources, which can be costly. Additionally, it mandates a relatively large size and highquality sample. The elements with high neutron absorption cross-sections also hinder clear scattering signals.  \nDespite the availability of direct measurement methods, the limitations mentioned make it challenging to identify magnetic order. Therefore, it would be beneficial to have a method for determining magnetic order that is more accessible and less expensive, even if it is not as direct as neutron scattering. For instance, specifying magnetic order based on the density of states (DOS), which can be accessed by various experimental methods, can be a compelling alternative. In principle, magnetic orders is closely connected with the particle-hole excitation spectrum and the DOS displays distinct features of the corresponding order. The challenge is how to extract and quantify the correlation effectively.  \nThe recent advancement of machine learning has hada significant impact in uncovering hidden correlations in the field of condensed matter physics [1–9] . This technology has also been applied to the study of magnetism,  \n∗ [chkim82@snu.ac.kr](chkim82@snu.ac.kr)[ ](chkim82@snu.ac.kr)† [arago@jnu.ac.kr](arago@jnu.ac.kr)  \nenabling for the prediction of physical quantities without the need for direct measurement or calculations, [10–23] or probing orders from the data [24–30] .  \nMotivated by the capability of machine learning to uncover complex relationships within numerical data, we explore the use of decision tree algorithms for identifying magnetic order from the density of states. We also examine an alternative probe through momentum-resolved spectra, ","cbCaisUuFJiDuKJl","https://ap.wps.com/l/cbCaisUuFJiDuKJl","pdf",10049190,1,"English","en",105,"# Introduction\n## Motivation and challenges of identifying magnetic order\n## Machine learning approach for magnetic classification\n# Model Hamiltonian\n## Unit cell and electronic structure of BaOsO3\n## First-principles calculations and Wannier-based setup\n# Data preparation and model performance\n# Conclusion and outlook","[{\"question\":\"Why is identifying magnetic order difficult with neutron scattering?\",\"answer\":\"Neutron scattering typically requires specialized facilities, costly experimental setups, relatively large high-quality samples, and it is further complicated by elements with high neutron absorption cross-sections.\"},{\"question\":\"What machine-learning method is used to classify magnetic order?\",\"answer\":\"The study uses decision-tree algorithms trained to identify antiferromagnetic orders from spin-integrated excitation spectra, including density of states and momentum-resolved spectral information.\"},{\"question\":\"Which factors most strongly influence model performance?\",\"answer\":\"The density of states provides good performance, but the spectral broadening method has a significant impact. Designing excitation energy as a machine-learning feature substantially improves classification quality and generalization.\"}]","Classification of magnetic order from electronic structure by using machine learning | PDF",1785720401,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"classification-of-magnetic-order-from-electronic-structure-by-using-machine-learning","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/classification-of-magnetic-order-from-electronic-structure-by-using-machine-learning/118814/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is identifying magnetic order difficult with neutron scattering?","Question",{"text":75,"@type":76},"Neutron scattering typically requires specialized facilities, costly experimental setups, relatively large high-quality samples, and it is further complicated by elements with high neutron absorption cross-sections.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine-learning method is used to classify magnetic order?",{"text":80,"@type":76},"The study uses decision-tree algorithms trained to identify antiferromagnetic orders from spin-integrated excitation spectra, including density of states and momentum-resolved spectral information.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors most strongly influence model performance?",{"text":84,"@type":76},"The density of states provides good performance, but the spectral broadening method has a significant impact. 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