[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121306-en":3,"doc-seo-121306-105":30,"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":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},121306,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Optimization of noncollinear magnetic ordering temperature in Y-type hexaferrite by machine learning","Searching the optimal doping compositions of Y-type hexaferrite Ba2Mg2Fe12O22 for enhanced non-collinear magnetic transition temperature (TNC) is addressed through a data-driven machine learning strategy instead of trial-and-error. A composition-property descriptor is constructed using SISSO (sure independence screening and sparsifying operator) to enable efficient, physically interpretable prediction. High-TNC candidates are identified, and BaSrMg0.28Co1.72Fe10Al2O22 is experimentally validated. Under suitable external magnetic field conditions, its TNC reaches 568 K and the magnetic transition temperature rises to 735 K, supporting a pathway to room-temperature single-phase multiferroics for device applications.","Optimization of noncollinear magnetic ordering temperature in Y-type hexaferrite by machine learning  \nYonghong Li, 1 Jing Zhang, 1 Linfeng Jiang,2 Long Zhang, 1 Yugang Zhang, 1 Xueliang Wu, 1 Yisheng Chai, 1,a) Xiaoyuan Zhou,2,a) and Zizhen Zhou2,a)  \n1Low Temperature Physics Laboratory, College of Physics, Chongqing University, Chongqing 401331, China  \n2 Center of Quantum Materials and Devices, Chongqing University, Chongqing 401331, China.  \na)Authors to whom correspondence should be addressed: [yschai@cqu.edu.cn](yschai@cqu.edu.cn), [xiaoyuan2013@cqu.edu.cn and ](xiaoyuan2013@cqu.edu.cn and zzzhou@cqu.edu.cn)[zzzhou@cqu.edu.cn](xiaoyuan2013@cqu.edu.cn and zzzhou@cqu.edu.cn)  \nAbstract:  \nSearching the optimal doping compositions of the Y-type hexaferrite Ba2Mg2Fe 12O22 remains a long-standing challenge for enhanced non-collinear magnetic transition temperature (TNC) . Instead of the conventional trial-and-error approach, the composition-property descriptor is established via a data driven machine learning method named SISSO (sure independence screening and sparsifying operator) . Based on the chosen efficient and physically interpretable descriptor, a series of Y-type hexaferrite compositions are predicted to hold high TNC, among which the BaSrMg0.28Co 1.72Fe 10Al2O22 is then experimentally validated. Test results indicate that, under appropriate external magnetic field conditions, the TNC of this composition reaches up to reaches up to 568 K, and its magnetic transition temperature is also elevated to 735 K. This work offers a machine learning-based route to develop room temperature single phase multiferroics for device applications.  \nMultiferroicity and the related magnetoelectric (ME) effects have gained significant interest in materials science. Magnetoelectric multiferroics, which exhibit coexistence of ferroelectric and magnetic orders and show strong ME effects, are particularly intriguing. The ME cross-coupling between these orders in such materials offers considerable promise for novel functional devices. 1,2 Among the various types of magnetoelectric multiferroics, hexaferrites are prominent, especially due to their robust magnetoelectric effects at temperatures up to room temperature.3-7  \nHexaferrites are categorized into six types: M, W, Y, Z, X, and U. 8,9 The Y-type hexagonal ferrite, represented by the chemical formula Ba2Me2Fe 12O22 (where Me can be Co2+, Zn2+, Ni2+, etc.),9 is the focus of extensive research. Several compositions with room temperature ME effects by non-collinear spin configurations are found previously.10-12 Figure 1(a) illustrates its crystal and magnetic structure. Its crystal  \n_  \nsymmetry is characterized by the R3m space group with 6 different sites for Fe and Me ions (3bVI, 6cIV *, 6cVI, 18hVI, 6cIV, and 3aIV sites) . Its magnetic structure consists of alternating large (L) and small (S) spin blocks along the c-axis. In these blocks, spins (Fe3+ and Me2+ ) are arranged antiferromagnetically, leading to the formation of large magnetic moments µL in theL blocks and small magnetic moments µS in the S blocks.13 This arrangement results in strong superexchange interactions and magnetic frustration at block boundaries, promoting various non-collinear magnetic structures at zero field. The confirmed non-collinear magnetic phases include proper screw, transverse conical, longitudinal conical, and alternative longitudinal conical phases. 14,15 Under an in-plane magnetic field, regardless of the initial phase, all these magnetic structures can transform into the transverse cone phases. Therefore, on one hand, the transverse cone phases can always persist up to TNC under a finite in-plane magnetic field. On the other hand, these transverse cone phases can host an in-plane polarization (P) via the inverse Dzyaloshinskii-Moriya (DM) interaction 16 or spin-current mechanism, 17 expressed as:􀜲~A ∑􀯜􀯝 􀝇 × (􀟤􀯅 × 􀟤􀯌) (k: propagation vector) . The in-plane magnetic field can easily tune the P vector","cbCaijIBXEKfjAyC","https://ap.wps.com/l/cbCaijIBXEKfjAyC","pdf",912374,1,22,"English","en",105,"# Abstract\n## Magnetoelectric multiferroics and Y-type hexaferrites\n## Non-collinear magnetic structures and polarization mechanism\n## Challenges from low TNC and role of ion doping\n## Data-driven SISSO approach for composition design\n## Predicted compositions and experimental validation","[{\"question\":\"Why is optimizing Y-type hexaferrite doping important in this work?\",\"answer\":\"Doping is used to raise the non-collinear magnetic transition temperature (TNC), which is often low in conventional Y-type hexaferrites and limits practical device applications.\"},{\"question\":\"What machine learning method is used to search for optimal compositions?\",\"answer\":\"The study uses SISSO (sure independence screening and sparsifying operator) to build a composition-property descriptor and predict candidates with high TNC.\"},{\"question\":\"Which composition was experimentally validated, and what transition temperatures were achieved?\",\"answer\":\"BaSrMg0.28Co1.72Fe10Al2O22 was validated. Under appropriate external magnetic field conditions, its TNC reaches up to 568 K, and the magnetic transition temperature is elevated to 735 K.\"}]","Optimization of noncollinear magnetic ordering temperature in Y-type hexaferrite by machine learning | PDF",1785734996,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimization-of-noncollinear-magnetic-ordering-temperature-in-y-type-hexaferrite-by-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimization-of-noncollinear-magnetic-ordering-temperature-in-y-type-hexaferrite-by-machine-learning/121306/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","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 optimizing Y-type hexaferrite doping important in this work?","Question",{"text":75,"@type":76},"Doping is used to raise the non-collinear magnetic transition temperature (TNC), which is often low in conventional Y-type hexaferrites and limits practical device applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning method is used to search for optimal compositions?",{"text":80,"@type":76},"The study uses SISSO (sure independence screening and sparsifying operator) to build a composition-property descriptor and predict candidates with high TNC.",{"name":82,"@type":73,"acceptedAnswer":83},"Which composition was experimentally validated, and what transition temperatures were achieved?",{"text":84,"@type":76},"BaSrMg0.28Co1.72Fe10Al2O22 was validated. Under appropriate external magnetic field conditions, its TNC reaches up to 568 K, and the magnetic transition temperature is elevated to 735 K.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]