[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119011-en":3,"doc-seo-119011-105":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},119011,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning - Dissertation","In today’s rapidly advancing technological landscape, powerful magnets underpin devices across advanced technology and green energy. Rare-earth magnets relying on elements such as neodymium or samarium face supply risks, motivating research to discover strong magnets without them. The work integrates efficient first-principles calculations with machine learning to accelerate experimental trial-and-error. It builds a magnetic materials database and improves a learning model to predict macroscopic and microscopic properties, validated via high-throughput calculations on Fe-Co-N, then applies adaptive feedback to reach new discoveries including Fe3CoB2.","Copyright by  \nTimothy Liao 2024  \n1  \nThe Dissertation Committee for Timothy Liao certifies that this is the approved version of the following dissertation:  \nThe discovery and design of rare-earth-free magnets using  \nmachine learning  \nCommittee:  \nJames R. Chelikowsky, Supervisor  \nAlexander A. Demkov  \nKeji Lai  \nFeliciano Giustino  \nGyeong S. Hwang  \nThe discovery and design of rare-earth-free magnets using  \nmachine learning  \nby  \nTimothy Liao  \nDissertation  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin May 2024  \nEpigraph  \nWhat starts here changes the world.  \n—University of Texas at Austin  \nAcknowledgments  \nFirst and foremost, I extend my heartfelt gratitude to my advisor, Prof. Jim Chelikowsky. The discussions with him are always interesting and his comments consistently offer profound insights. I am also deeply appreciative of the support and encouragement provided by my dissertation committee members: Profs. Alex Demkov, Keji Lai, Feliciano Giustino, and Gyeong Hwang.  \nI am indebted to Prof. Cai-Zhuang Wang and Dr. Masahiro Sakurai for graciously welcoming me to participate in the collaborative project focused on the search for rare-earth free magnets. My eye-opening one-year visit at Ames National Laboratory would not have been possible without the warm hospitality extended by Prof. Wang and the Department of Energy Office of Science Graduate Student Research (SCGSR) Program. I am grateful for the weekly meetings with Drs. Weiyi Xia, Renhai Wang, Chao Zhang, and Huaijun Sun, which have propelled my research progress. Additionally, I am thankful for the invaluable conceptualization of the project by Profs. Kai-Ming Ho, David Sellmyer, Xiaoshan Xu, and Dr. Balamurugan Balasubramanian.  \nI owe a debt of gratitude to my lab mate, Dr. Kai-Hsin Liou, for providing extensive guidance on the parsec code and high-performance computing. I appreciate the constructive research feedback received from Drs. Dingxin Fan, Weiwei Gao, and Yuki Sakai. I am also thankful for the enriching discussions with Josh Neitzel, Drs. Charles Lena, Mehmet Dogan, Zhao Tang, Wei Shen Tee, and Deena Roller. I appreciate Robert Hoelscher for efficiently taking care of administrative matters.  \nI thank my parents’ support during my academic journey. Additionally, I extend my thanks to my housemates, friends, and all those who have provided care and support both in Austin and in Ames.  \nAbstract  \nThe discovery and design of rare-earth-free magnets using  \nmachine learning  \nTimothy Liao, PhD  \nThe University of Texas at Austin, 2024  \nSUPERVISOR: James R. Chelikowsky  \nIn today’s rapidly advancing technological landscape, the reliance on powerful magnets spans various sectors, including advanced technology and green energy.  \nThese magnets, integral to the functionality of devices like wind turbines, electric cars, and computer hard disks, often feature rare-earth elements such as neodymium or samarium. Given the supply risks associated with rare-earth elements, my dissertation research focuses on discovering strong magnets without them. I leverage efficient first principles calculations and machine learning to accelerate the typically slow trial-and-error method in experiments. This presentation outlines the efforts, culminating in the discovery of a new material, Fe3 CoB2 .  \nInitially, I establish a magnetic materials database encompassing magnetization (i.e., magnetic strength), magnetic anisotropy (i.e., magnetic field resilience), and Curie temperature (i.e., heat resilience) . Then, I enhance the functionality of a popular machine learning model to predict both macroscopic and microscopic properties, validated through high-throughput first-principles calculations on Fe-Co-N.  \nFollowing successful machine learning model testing, I introduce an adaptive machine learning feed","cbCaiquM8nEnG6sD","https://ap.wps.com/l/cbCaiquM8nEnG6sD","pdf",21622013,1,174,"English","en",105,"# Chapter 1: Dissertation Overview\n# Chapter 2: Theoretical Methods for the Design and Discovery of Materials\n## 2.1 The Need for Rare-Earth-Free Permanent Magnets\n## 2.2 Magnetic Materials Database\n## 2.3 Adaptive Machine Learning Feedback\n## 2.4 Density Functional Theory\n## 2.5 Machine Learning Model\n## 2.6 Adaptive Genetic Algorithm\n# Chapter 3: Discovering Rare-Earth-Free Magnetic Materials through the Development of a Database\n## 3.1 Introduction\n## 3.2 Methods for Data Generation\n## 3.3 Database Overview\n## 3.4 Description of Dataset","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It addresses the need for strong permanent magnets without rare-earth elements due to supply risks associated with rare-earth materials like neodymium and samarium.\"},{\"question\":\"How does the research use machine learning to speed up discovery?\",\"answer\":\"It builds a magnetic materials database and enhances a machine learning model to predict magnetic properties, then validates predictions using high-throughput first-principles calculations.\"},{\"question\":\"What is the adaptive machine learning feedback system and what did it achieve?\",\"answer\":\"It is a closed-loop framework that streamlines computational discovery and experimental synthesis, reducing the discovery process for Fe3CoB2 to a matter of days.\"}]","The Discovery and Design of Rare-Earth-Free Magnets Using Machine Learning - Dissertation | PDF",1785721858,438,{"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},"the-discovery-and-design-of-rare-earth-free-magnets-using-machine-learning-dissertation","",{"@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/the-discovery-and-design-of-rare-earth-free-magnets-using-machine-learning-dissertation/119011/",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},"What problem does the dissertation address?","Question",{"text":75,"@type":76},"It addresses the need for strong permanent magnets without rare-earth elements due to supply risks associated with rare-earth materials like neodymium and samarium.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research use machine learning to speed up discovery?",{"text":80,"@type":76},"It builds a magnetic materials database and enhances a machine learning model to predict magnetic properties, then validates predictions using high-throughput first-principles calculations.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the adaptive machine learning feedback system and what did it achieve?",{"text":84,"@type":76},"It is a closed-loop framework that streamlines computational discovery and experimental synthesis, reducing the discovery process for Fe3CoB2 to a matter of days.","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"]