[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127545-en":3,"doc-seo-127545-105":31,"detail-sidebar-cat-0-en-105":84},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127545,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Predictions of High-Curie-Temperature Materials","Room-temperature magnetism relies on materials with high Curie temperature (TC), yet discovering such compounds is constrained by the large candidate space and costly fabrication and testing. Using two large experimental TC datasets, machine-learning models predict TC from chemical composition alone. Random forest and k-NN are trained on 2,500+ samples, validated on 3,000+ entries, and compared across descriptor and regression choices. Random forest achieves the highest accuracy, cobalt- and iron-rich chemistries yield the top TC trends, and analysis reveals systematic over- and under-prediction bias plus guidance on data growth or descriptor efficiency for exhaustive searches.","arXiv :2307 .06879v1 [ cond-mat .mtrl-sci ] 13 Jul 2023  \nMachine Learning Predictions of High-Curie-Temperature Materials  \nJoshua F. Belot,1 Valentin Taufour,2 Stefano Sanvito,3 and Gus L. W. Hart4  \n1) Brigham Young University, Provo, UT, USA, 84602  \n2) University of California, Davis, One Shields Avenue, Davis, CA, USA, 95616  \n3) School of Physics, AMBER and CRANN Institute, Trinity College, Dublin 2, Ireland  \n4) Brigham Young University, Provo, UT, USA, 84602  \n(*Electronic mail: [gus.hart@byu.edu](gus.hart@byu.edu))  \n(Dated: 14 July 2023)  \nTechnologies that function at room temperature often require magnets with a high Curie temperature, TC, and can be improved with better materials. Discovering magnetic materials with a substantial TC is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known data sets of experimental Curie temperatures, we develop machine-learning models to make rapid TC predictions solely based on the chemical composition of a material. We train a random forest model and a k-NN one and predict on an initial dataset of over 2,500 materials and then validate the model on a new dataset containing over 3,000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-TC materials and under-predict high-TC materials. For exhaustive searches to find new high-TC materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.  \nFerromagnetic materials are essential in modern technologies especially in energy production and data storage. The threshold temperature where magnetism disappears is called the Curie temperature, TC . Searches for high-TC materials typically look for magnets with a TC of at least 550–600 K, which is required for reliably running an application at room temperature.1,2 High-TC magnets are valuable but rare, 1,3,4 in particular when other properties are required on their electronic structure.5 Although some empirical rules for the design of new magnets exist6 , rapid predictions of Curie temperatures could assist in identifying candidate high-TC magnets in large-scale screening exercises. Thousands of known ferromagnetic materials exist7 , and while high-throughput computation can help in identifying hundreds of thousands of potential magnets, experience suggests that only a fraction of them can actually be realized.3,8–10  \nMeasuring the Curie temperature of a compound is a relatively standard and accurate procedure, but of course needs the material to be made first. In contrast, in a computational design process, the TC must be predicted ahead of experiments, only using physical and chemical information. This is a complex task prone to large errors. In fact, one needs to compute the elementary magnetic excitations of a compound, most typically from density functional theory (DFT), map these on a simple model, usually a Heisenberg-type one, and then perform thermodynamic sampling with Monte Carlo methods.11–26 Then, the choice of DFT functional, the quality and appropriateness of the mapping, and subtleties in the Monte Carlo algorithms, all contribute to a large uncertainty on the predictions. Often this uncertainty is so severe that blind predictions of TC for unknown compounds are almost impossible to make.  \nMachine-learning algorithms capture complex relationships in data that may be difficult to recognize or  \nunderstand.27 Even though mac","cbCaipGeJ4eIJMG6","https://ap.wps.com/l/cbCaipGeJ4eIJMG6","pdf",1659888,2,1,9,"English","en",105,"# Background\n## Curie temperature and high-TC magnets\n## Challenges in discovering high-TC materials\n# Data and Modeling Approach\n## Machine-learning TC prediction from composition\n## Training, validation, and datasets\n# Model Evaluation and Findings\n## Descriptor and regression comparisons\n## Accuracy results and best-performing model\n## Composition trends in binary and ternary compounds\n# Error Analysis and Search Guidance\n## Systematic prediction bias\n## Learning-rate insights for finding new materials","[{\"question\":\"Which model performs best, and what descriptor choices matter most?\",\"answer\":\"A random forest model provides the most accurate predictions. Accuracy is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties.\"}]","Machine Learning Predictions of High-Curie-Temperature Materials | PDF",1785939888,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"machine-learning-predictions-of-high-curie-temperature-materials","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-predictions-of-high-curie-temperature-materials/127545/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Which model performs best, and what descriptor choices matter most?","Question",{"text":76,"@type":77},"A random forest model provides the most accurate predictions. Accuracy is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,90,94,98,103,108,113,116,120,123,127],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":111,"slug":112},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":114,"slug":115},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":99,"slug":130},19,"General","general"]