[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127011-en":3,"doc-seo-127011-105":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},127011,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs","The discovery of novel quantum materials within ternary phase spaces containing antagonistic pair elements such as Fe with Bi, Pb, In, and Ag is both challenging and promising. This study integrates cerium (Ce) into Fe-X systems to stabilize immiscible pairs and addresses the open question of which third element and composition ratio enable ternary stabilization. A machine-learning guided framework using crystal graph convolutional neural networks (CGCNN) with first-principles calculations efficiently explores composition/structure space, predicting 9 stable and 37 metastable Ce-Fe-X compounds, and evaluates their structural and energetic properties for potential quantum-material applications and alternatives to critical rare earth elements.","Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs  \nWeiyi Xia 1,2, Wei-Shen Tee2,1, Paul C. Canfield 1,2, Fernando Assis Garcia 1,2, Raquel D Ribeiro 1,2, Yongbin Lee1,2, Liqin Ke1,2, Rebecca Flint 1,2, and Cai-Zhuang Wang1,2,*  \n1Ames National Laboratory, U.S. Department of Energy, Iowa State University, Ames, Iowa 50011, USA  \n2Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA  \n* [wangcz@ameslab.gov](wangcz@ameslab.gov)  \nAbstract  \nThe discovery of novel quantum materials within ternary phase spaces containing antagonistic pair such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the stabilization of these immiscible pairs through the integration of Cerium (Ce), an abundant rare-earth and cost-effective element. By employing a machine learning (ML)-guided framework, particularly crystal graph convolutional neural networks (CGCNN), combined with first-principles calculations, we efficiently explore the composition/structure space and predict 9 stable and 37 metastable Ce-Fe-X (X=Bi, Pb, In and Ag) ternary compounds. Our findings include the identification of multiple new stable and metastable phases, which are evaluated for their structural and energetic properties. These discoveries not only contribute to the advancement of quantum materials but also offer viable alternatives to critical rare earth elements, underscoring the importance ofCe-based intermetallic compounds in technological applications.  \n1. Introduction  \nAn antagonistic pair, or immiscible pair, is associated with having immiscibility over almost the whole composition range, under a reasonable temperature (e.g., melting temperature) [1] . Regions of ternary phase space involving two immiscible elements, such as Fe with Pb, Bi, Ag, In, etc., remain relatively unexplored but hold great promise for novel quantum materials discovery. Ternary intermetallic compounds containing immiscible pairs are rare, yet when they do form, the immiscible elements typically segregate, with a third element encapsulating or separating them [1] . This often results in reduced dimensionality, leading to unique one-dimensional (1D) or two-dimensional (2D) structures. Forcing a 3d-transition metal (TM) like Fe to adopt such reduced dimensionality can induce complex electronic and magnetic states, including superconductivity [2], fragile magnetism [3], antiferromagnetic [4] or even ferromagnetism [1] . Especially, a recent experimental discovery of La4Co4Pb shows distinct substructures involving antagonistic pairs, where the Co atoms adopt a corrugated Kagome net that supports itinerant antiferromagnetism [4] .  \nThe central question is: \"Which third element, and in what ratio, can stabilize ternary compounds containing immiscible pairs?\" To address this, a thorough understanding of the relationship between chemical compositions, crystal structures, and their relative thermodynamic stability is crucial.  \nCe-based intermetallic compounds enter this context as promising candidates for the third element. Ce is abundant, cost-effective, and has shown potential in replacing critical rare earth (RE) elements in various technological applications, particularly in clean energy and high-performance magnets [5-10] . High-performance magnets, essential in energy generation, conversion, and information storage devices, traditionally rely on critical rare earth elements such as Nd, Sm, and Dy. The insecure supply and high costs of these elements have spurred significant interest in finding alternatives. Ce, being more abundant and cost-effective, presents a viable substitute. Notably, recent studies have shown that replacing Sm with Ce and partially substituting Co with the non-magnetic element Cu can yield CeCo5-xCux alloy with desirable magnetic properties for permanent magnet applications [11-18] .  \nIn our research, we hypothesize that Ce can","cbCaindLEXfXlPWR","https://ap.wps.com/l/cbCaindLEXfXlPWR","pdf",1221875,1,20,"English","en",105,"# Introduction\n## Antagonistic (immiscible) pairs in ternary phase space\n## Role of cerium in stabilizing ternary compounds\n## ML-guided framework and first-principles integration\n# Methods and Algorithms\n## CGCNN-based screening and prediction\n# Results\n## Structural and energetic properties of predicted phases","[{\"question\":\"Why are Fe-based antagonistic pairs difficult to study in ternary systems?\",\"answer\":\"Antagonistic (immiscible) element pairs exhibit immiscibility across most composition ranges under reasonable temperatures, making stabilized ternary phases rare. Even when they form, the immiscible elements tend to segregate, often producing reduced-dimensional structures.\"},{\"question\":\"What role does cerium (Ce) play in this work?\",\"answer\":\"Ce is introduced as the third element to stabilize ternary compounds containing immiscible Fe-X pairs. The study motivates Ce by its abundance and cost-effectiveness as a potential alternative to critical rare earth elements.\"},{\"question\":\"How does the proposed machine learning approach accelerate compound discovery?\",\"answer\":\"The framework uses CGCNN to learn from known crystal structure representations and efficiently screen composition/structure candidates. It is coupled with first-principles calculations to evaluate structural and energetic properties, leading to predictions of stable and metastable phases.\"}]","Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs | PDF",1785936302,50,{"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-accelerated-prediction-of-ce-based-ternary-compounds-involving-antagonistic-pairs","",{"@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-accelerated-prediction-of-ce-based-ternary-compounds-involving-antagonistic-pairs/127011/",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},"Why are Fe-based antagonistic pairs difficult to study in ternary systems?","Question",{"text":75,"@type":76},"Antagonistic (immiscible) element pairs exhibit immiscibility across most composition ranges under reasonable temperatures, making stabilized ternary phases rare. Even when they form, the immiscible elements tend to segregate, often producing reduced-dimensional structures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does cerium (Ce) play in this work?",{"text":80,"@type":76},"Ce is introduced as the third element to stabilize ternary compounds containing immiscible Fe-X pairs. The study motivates Ce by its abundance and cost-effectiveness as a potential alternative to critical rare earth elements.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed machine learning approach accelerate compound discovery?",{"text":84,"@type":76},"The framework uses CGCNN to learn from known crystal structure representations and efficiently screen composition/structure candidates. It is coupled with first-principles calculations to evaluate structural and energetic properties, leading to predictions of stable and metastable phases.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","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":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]