[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121016-en":3,"doc-seo-121016-105":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},121016,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","HIGH YIELD STRENGTH IN HIGH-ENTROPY ALLOYS DESIGNED - A Thesis - Cornell University Master of Science","High Entropy Alloys (HEAs) offer a powerful new paradigm for alloy design, providing superior material performance over conventional alloys. Designing HEAs with many metallic elements and determining their molar fractions remains difficult. This thesis presents a machine-learning driven workflow to identify new HEAs with high yield strength. It covers data collection and preprocessing from online sources, then evaluates five machine learning models, hyperparameter tuning methods, and optimization algorithms. Results are assessed by yield-strength prediction accuracy and by how well molar fractions are selected to achieve high strength. The study supports the approach as a practical reference for experimental HEA design.","HIGH YIELD STRENGTH IN HIGH-ENTROPY ALLOYS DESIGNED  \nWITH MACHINE-LEARNING METHODS  \nA Thesis  \nPresented to the Faculty of the Graduate School  \nof Cornell University  \nIn Partial Fulfillment of the Requirements for the Degree of  \nMaster of Science  \nby  \nChanghwi Han  \n© 2023 Changhwi Han  \nABSTRACT  \nHigh Entropy Alloys (HEAs) have emerged as a novel paradigm in alloy design due to their superior material properties compared to the normal alloys. However, designing the HEAs with multiple metallic elements and their molar fractions is a challenging task. To resolve this issue, this project proposes a modern approach for designing new HEAs with high yield strength by utilizing machine learning techniques. The study outlines a comprehensive process for collecting and pre-processing dataset from online resources, and explores the application of five different machine learning models, two hyperparameter tuning methods, and two optimization algorithms to design HEAs. The effectiveness of each approach is evaluated based on their ability to predict the yield strength of HEAs and determine the required molar fractions of metallic elements to obtain high yield strength. The findings ofthis study suggest that the proposed method can serve as a valuable reference in the experimental design process of HEAs.  \nBIOGRAPHICAL SKETCH  \nChanghwi Han was born in 1990 in Tongyeong-si, South Korea. After graduating from Kyeongil High School, he served in the R.O.K. Navy before pursuing his academic and professional goals.  \nTo earn money for college tuition, he moved to Australia and worked at a meat factory. Eventually, he moved to the United States and earned a Bachelor’s degree in Mechanical Engineering from the University of California, Los Angeles in 2017.  \nUpon graduation, he returned to South Korea to work as a Mechanical Engineer at Samsung Electronics in the consumer electronics industry. After four years, he was admitted to Cornell University to pursue a Master of Science degree in Mechanical Engineering.  \nUnder the guidance of Professor Jingjie Yeo, Changhwi focused on designing High Entropy Alloys using machine learning techniques to achieve the highest yield strength. He is currently applying his knowledge and skills to develop innovative solutions in the field of mechanical engineering.  \nACKNOWLEDGMENTS  \nFirst and foremost, I would like to express my sincere gratitude to my thesis advisor, Professor Jingjie Yeo, for his exceptional support, guidance, and patience throughout the entire thesis project process. Whenever I encountered challenged or obstacles, Professor Yeo was always available in his office or via online meetings for insightful discussions and guidance. His constructive feedback and suggestions for my work have been invaluable in shaping my research project and writing. Whether he intended or not, in particular, his guidance on allowing students to conduct research independently has enabled me to develop a range of useful data analytic skills.  \nI am also grateful to the staff of the Department of Mechanical Engineering for their assistance, resources, and encouragement throughout my graduate studies, especially to Lataya Fann, the Graduate Field Administrator, for her support and guidance throughout the program.  \nLastly, I would like to express my deep appreciation to my parents for their unwavering love, support, and encouragement throughout my academic journey. Their support during my undergraduate studies in the United States and their constant encouragement to pursue graduate school have been valuable in my success. This achievement would not have been possible without their love and support.  \nTABLE OF CONTENTS  \nABSTRACT……………………………………………………………………………………...iii  \nBIOGRAPHICAL SKETCH……………………………………………………………………..iv  \nACKNOWLEDGMENTS………………………………………………………………………...v  \nTABLE OF CONTENTS…………………………………………………………………………vi  \nLIST OF FIGURES……………………………………………………………………………...vii  \nLIST OF TABLES………………………………………………………………………………","cbCaibL2gPnjSqfu","https://ap.wps.com/l/cbCaibL2gPnjSqfu","pdf",1091743,1,50,"English","en",105,"# ABSTRACT\n# BIOGRAPHICAL SKETCH\n# ACKNOWLEDGMENTS\n# TABLE OF CONTENTS\n# LIST OF FIGURES\n# LIST OF TABLES\n# LIST OF SYMBOLS & ABBREVIATIONS\n# 1. INTRODUCTION\n# 2. METHODOLOGY\n## 2.1 Data Selection\n## 2.2 Data Preprocessing\n## 2.3 Machine Learning Models\n## 2.4 LAMMPS\n# 3. RESULTS AND DISCUSSION\n## 3.1 Hyperparameter Model Performance\n## 3.2 Prediction Error of HEAs in the Five Machine Learning Models\n## 3.3 Prediction Error of AlNbTiV & NbMoTaW with Different Feature Weights\n## 3.4 LAMMPS Simulation Results for Yield Strength of the Four HEAs\n## 3.5 Comparison between Machine Learning Optimized Results with LAMMPS Results\n# 4. CONCLUSIONS\n# REFERENCES","[{\"question\":\"Why are high entropy alloys (HEAs) challenging to design for high yield strength?\",\"answer\":\"HEAs involve multiple metallic elements and their molar fractions must be determined jointly. This makes the design task complex and difficult to optimize directly.\"},{\"question\":\"What machine learning workflow is used to design HEAs in this thesis?\",\"answer\":\"The study collects and preprocesses dataset entries from online resources, then trains five machine learning models. It also applies two hyperparameter tuning methods and two optimization algorithms to guide design.\"},{\"question\":\"How is model effectiveness evaluated?\",\"answer\":\"Effectiveness is measured by each approach’s ability to predict HEA yield strength and to determine molar fractions of constituent elements that target high yield strength.\"}]","HIGH YIELD STRENGTH IN HIGH-ENTROPY ALLOYS DESIGNED - A Thesis - Cornell University Master of Science | PDF",1785733324,126,{"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},"high-yield-strength-in-high-entropy-alloys-designed-a-thesis-cornell-university-master-of-science","",{"@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/high-yield-strength-in-high-entropy-alloys-designed-a-thesis-cornell-university-master-of-science/121016/",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},"Why are high entropy alloys (HEAs) challenging to design for high yield strength?","Question",{"text":75,"@type":76},"HEAs involve multiple metallic elements and their molar fractions must be determined jointly. This makes the design task complex and difficult to optimize directly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning workflow is used to design HEAs in this thesis?",{"text":80,"@type":76},"The study collects and preprocesses dataset entries from online resources, then trains five machine learning models. It also applies two hyperparameter tuning methods and two optimization algorithms to guide design.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model effectiveness evaluated?",{"text":84,"@type":76},"Effectiveness is measured by each approach’s ability to predict HEA yield strength and to determine molar fractions of constituent elements that target high yield strength.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":21,"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":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"]