[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121320-en":3,"doc-seo-121320-105":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},121320,1099514067438,"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","Machine learning-based prediction of FeNi nanoparticle magnetization - Research study","This work proposes a computationally efficient approach for estimating the magnetization of Fe0.7Ni0.3 bodycentered cubic nanoparticles at room temperature using machine-learning algorithms, expressed via the average magnetic moment per atom ⟨μ⟩. Magnetization data for isolated nanoparticles with varied shapes and FeNi distributions were generated using atomistic spin dynamics simulations, producing 1600+ samples split into training and testing sets (70%–30%). Features derived from surface/core atom counts, energy distributions, pair correlation functions, and coordination statistics. Random Forest, Elastic Net, SVR, and CatBoost were evaluated, with CatBoost and RF reaching R2 up to 0.86, while interface effects and Fe presence were identified as key drivers. Results reduce time and memory versus traditional ASD, enabling rapid screening for target magnetic properties for technological uses.","Journal of Materials Research and Technology 33 (2024) 5263–5276  \nContents lists available at ScienceDirect  \nJournal of Materials Research and Technology  \njournal [homepage: www.elsevier.com/locate/jmrt](homepage: www.elsevier.com/locate/jmrt)  \n| Machine learning-based prediction of FeNi nanoparticle magnetization |  |  |  |\n| --- | --- | --- | --- |\n| Federico Williamson a, Nadhir Naciffb, Carlos Catania c,a, Gonzalo dos Santos c,b, Nicol´as Amigod, Eduardo M. Bringa a,b,c,e,*\u003Cbr>a Facultad de Ingeniería, Universidad Nacional de Cuyo, Argentina b Facultad de Ingeniería, Universidad de Mendoza, Mendoza, 5500, Argentina c CONICET, Mendoza, 5500, Argentina\u003Cbr>d Departamento de Física, Facultad de Ciencias Naturales, Matem´atica y del Medio Ambiente, Universidad Tecnol´ogica Metropolitana, Las Palmeras 3360, ˜Nu˜noa, 7800003, Santiago, Chile\u003Cbr>e Centro de Nanotecnolog’ıa Aplicada, Universidad Mayor, Santiago, Chile |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling editor: Carlos Ruestes |  | This work proposes a computationally efficient approach for estimating the magnetization of Fe0.7Ni0.3 bodycentered cubic (bcc) nanoparticles (NPs) at room temperature using machine-learning algorithms, in terms of the average magnetic moment per atom, 〈μ〉 . The magnetization data of isolated NPs were generated using atomistic spin dynamics (ASD) simulations for various nanoparticle shapes (cubes, spheres, octahedra, cones, cylinders, ellipsoids, flakes, and pyramids, with or without nanovoids) and FeNi distributions (random, coreshell, onion, sandwich, and Janus with different boundary planes). More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. The best-performing models, CatBoost and RF, achieved R2 scores of up to 0.86, demonstrating their accuracy in predicting NP magnetization. Feature analysis highlighted the significance of the interface between Fe and Ni clusters, Fe–Fe interactions, and the presence of Fe on the surface as critical contributors to overall magnetization. Random alloy spherical NPs without porosity exhibited the highest 〈μ〉 ~ 1.6μB due to reduced Ni–Ni interactions. Applying machine-learning methods significantly reduces computational time and memory requirements compared to traditional ASD simulations. This allows for rapid prediction of NPs with desired magnetic properties, making them suitable for various technological applications. |  |\n| Keywords:\u003Cbr>Magnetization Nanoparticle\u003Cbr>Machine learning Atomistic spin dynamics FeNi |  |  |  |\n\n1. Introduction  \nInvestigation of materials at the nanoscale has gained importance due to their unique properties and technological potential. Ferromagnetic alloy nanoparticles (NPs) hold promise in medicine, serving as biomarkers, drug carriers, and tools for cellular labeling and hyperthermia in cancer treatments [1–3]. Understanding the magnetic properties of NPs is key to optimizing their practical use, with experimental studies showing a relationship between NP size and magnetization [4–6].  \nResearchers are increasingly focused on optimizing magnetic properties, such as raising the Curie temperature [7]. Soft magnetic materials (SMMs), like Fe–Co and Fe–Ni, show great potential in hyperthermia  \ntreatments due to their responsiveness to magnetic fields, releasing heat efficiently into cancer cells [8]. Their high permeability and low coercivity, make SMMs ideal for energy conversion applications [9]. To fully harness their biomedical applications, such as drug delivery and MRI, understanding their magnetic behavior is crucia","cbCaijoauxuuSMvI","https://ap.wps.com/l/cbCaijoauxuuSMvI","pdf",6107577,1,14,"English","en",105,"# Abstract\n# Introduction\n## Importance of nanoscale magnetic materials\n## Single-domain behavior and synthesis advances\n## Simulation methods: MD, ASD, and SLD\n## Motivation: linking magnetization to structure and coordination","[{\"question\":\"How are the FeNi nanoparticle magnetization data generated for model training?\",\"answer\":\"Magnetization data for isolated Fe0.7Ni0.3 bcc nanoparticles are generated using atomistic spin dynamics (ASD) simulations across multiple nanoparticle shapes and FeNi distributions.\"},{\"question\":\"Which machine-learning algorithms are used to predict the average magnetic moment per atom?\",\"answer\":\"Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost) are applied to predict the average magnetic moment per atom ⟨μ⟩.\"},{\"question\":\"What model performance is reported for the best predictors and what factors matter most?\",\"answer\":\"CatBoost and RF achieve R2 scores up to 0.86. Feature analysis highlights the Fe–Ni interface, Fe–Fe interactions, and Fe atoms on the surface as critical contributors to overall magnetization.\"}]","Machine learning-based prediction of FeNi nanoparticle magnetization - Research study | PDF",1785735058,35,{"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},"machine-learning-based-prediction-of-feni-nanoparticle-magnetization-research-study","",{"@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/machine-learning-based-prediction-of-feni-nanoparticle-magnetization-research-study/121320/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How are the FeNi nanoparticle magnetization data generated for model training?","Question",{"text":75,"@type":76},"Magnetization data for isolated Fe0.7Ni0.3 bcc nanoparticles are generated using atomistic spin dynamics (ASD) simulations across multiple nanoparticle shapes and FeNi distributions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning algorithms are used to predict the average magnetic moment per atom?",{"text":80,"@type":76},"Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost) are applied to predict the average magnetic moment per atom ⟨μ⟩.",{"name":82,"@type":73,"acceptedAnswer":83},"What model performance is reported for the best predictors and what factors matter most?",{"text":84,"@type":76},"CatBoost and RF achieve R2 scores up to 0.86. Feature analysis highlights the Fe–Ni interface, Fe–Fe interactions, and Fe atoms on the surface as critical contributors to overall magnetization.","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"]