[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125344-en":3,"doc-seo-125344-105":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":4,"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},125344,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Interpretable machine learning-guided design of Fe-based soft magnetic alloys - Abstract","A machine-learning guided approach predicts saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, focusing on Fe-Si-B systems. ML models trained on experimental data show increasing Si and B reduce MS from about 1.81 T to ~1.54 T, supported by experimental validation on Fe-1Si-1B, Fe-5Si-5B, and Fe-10Si-10B compositions. Uncertainty quantification and interpretability reveal MS depends on a nonlinear interplay of Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions. The framework is extended to pseudo-quaternary Fe-Si-B/Cr/Cu/Zr/Nb spaces and indicates potential for Co- and Ni-free high-performance soft magnets.","arXiv copy [material-science] 28 Apr 2025  \narXiv: 04/28/2025 Nachnani et al.  \nInterpretable machine learning-guided design of Fe-based soft magnetic alloys  \nAditi Nachnani,1 Kai K. Li-Caldwell,1 Saptarshi Biswas,1 Prince Sharma,1  \nGaoyuan Ouyang,1 and Prashant Singh1,*  \n1Ames National Laboratory, U.S. Department of Energy, Iowa State University, Ames, IA 50011, USA  \nAbstract  \nWe present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT~2.04 T) to ~1 .54 T (DFT~1 .56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2 .09T), Fe-5Si-5B (2 .01T) and Fe-10Si-10B (1 .54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5 B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5 B9 Cu1 Nb3), NANOPERM (Fe88Zr7 B4Cu1), and HITPERM (Fe44Co44Zr7 B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co-and Ni-free, soft magnetic materials.  \nKeywords: Soft-magnets, Fe-alloys, AI/ML, Interpretable ML, Uncertainty quantification, DFT, VSM  \n*Corresponding author Email: [psingh84@ameslab.gov/prashant40179@gmail.com](psingh84@ameslab.gov/prashant40179@gmail.com)  \narXiv: 04/28/2025 Nachnani et al.  \n1. Introduction:  \nThe quest for greater efficiency in energy conversion and transformation has driven the development of advanced energy materials, with soft magnetic materials playing a pivotal role [1-5] . These materials, characterized by their low coercivity and ability to rapidly respond to magnetic fields, are essential for minimizing energy loss and directing magnetic flux in electromagnetic devices. Their applications range from transformers and inductors to motors and generators, making them indispensable components in modern energy and transportation technologies [6-9] .  \nIron-based soft magnetic materials, including silicon steels, non-oriented steels, amorphous alloys, and nanocrystalline materials, exhibit tailored properties for diverse applications. Silicon steels, widely used in transformer cores, achieve saturation magnetizations of 2.0-2.1 T and coercivity values of 100-1000 A/cm [3], with performance closely tied to their grain-oriented structure that minimizes magnetic anisotropy along specific crystallographic directions. Non-oriented steels, designed for isotropic magnetic performance in rotating machinery, feature grain sizes of 10-50 μm, with coercivity values of 500-1500 A/cm and comparable saturation magnetization [10] . Amorphous materials, such as Fe-based metallic glasses, produced via rapid solidification, offer saturation magnetizations of 1.2-1.6 T and coercivity below 100 A/cm due to their disordered atomic structure [11], making them ideal for high-frequency applications. Nanocrystalline alloys combine ultra-fine grain sizes of 10-20 nm with saturation magnetizations of 1.2-1.3 T and coerci","cbCaiacMozaIi1RH","https://ap.wps.com/l/cbCaiacMozaIi1RH","pdf",2905993,1,24,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What magnetic properties does the machine-learning framework target in the paper?\",\"answer\":\"It predicts saturation magnetization (MS) and coercivity (HC) for Fe-rich soft magnetic alloys, especially Fe-Si-B-based compositions.\"},{\"question\":\"How do Si and B additions affect saturation magnetization in Fe-Si-B alloys?\",\"answer\":\"Increasing Si and B reduces MS, with reported trends from about 1.81 T down to around ~1.54 T across Fe-Si-B compositions.\"},{\"question\":\"What factors are most influential for MS and HC according to the interpretability results?\",\"answer\":\"MS is governed by a nonlinear interplay involving Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows.\"}]","Interpretable machine learning-guided design of Fe-based soft magnetic alloys - Abstract | PDF",1785898313,60,{"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},"interpretable-machine-learning-guided-design-of-fe-based-soft-magnetic-alloys-abstract","",{"@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/interpretable-machine-learning-guided-design-of-fe-based-soft-magnetic-alloys-abstract/125344/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What magnetic properties does the machine-learning framework target in the paper?","Question",{"text":75,"@type":76},"It predicts saturation magnetization (MS) and coercivity (HC) for Fe-rich soft magnetic alloys, especially Fe-Si-B-based compositions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do Si and B additions affect saturation magnetization in Fe-Si-B alloys?",{"text":80,"@type":76},"Increasing Si and B reduces MS, with reported trends from about 1.81 T down to around ~1.54 T across Fe-Si-B compositions.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors are most influential for MS and HC according to the interpretability results?",{"text":84,"@type":76},"MS is governed by a nonlinear interplay involving Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]