[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128328-en":3,"doc-seo-128328-105":31,"detail-sidebar-cat-0-en-105":92},{"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},128328,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Data-Driven Discovery of Extreme Thermal Materials by High-Throughput Computation and Machine Learning","The pursuit of materials with extraordinary properties drives progress in materials science and engineering, yet finding truly extreme candidates remains challenging. This dissertation develops a synergistic workflow that integrates machine learning with density functional theory to push beyond existing discovery limits. Lattice thermal conductivity serves as the primary case study, using iterative training, screening, and verification to expand candidate pools and identify low-LTC materials. A structure-generation strategy using MAGUS further overcomes database constraints and enables discovery of previously untested, dynamically stable crystals.","University of South Carolina  \nScholar Commons  \nTheses and Dissertations  \nFall 2024  \nData-Driven Discovery of Extreme Thermal Materials by High-Throughput Computation and Machine Learning  \nJoshua Ojih  \nUniversity of South Carolina  \nFollow this and additional works at: [https://scholarcommons.sc.edu/etd](https://scholarcommons.sc.edu/etd)  \n Part of the Mechanical Engineering Commons  \nRecommended Citation  \nOjih, J. (2024) . Data-Driven Discovery of Extreme Thermal Materials by High-Throughput Computation and Machine Learning. (Doctoral dissertation) . Retrieved from [https://scholarcommons.sc.edu/etd/8145](https://scholarcommons.sc.edu/etd/8145)  \n[This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in](This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in)[ ](This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in)[Theses and Dissertations by an authorized administrator of Scholar Commons. For more information](Theses and Dissertations by an authorized administrator of Scholar Commons. For more information), please [contact](contact digres@mailbox.sc.edu)[ digres@mailbox.sc.edu](contact digres@mailbox.sc.edu).  \nDATA-DRIVEN DISCOVERY OF EXTREME THERMAL MATERIALS BY HIGH-THROUGHPUT COMPUTATION AND MACHINE  \nLEARNING  \nby  \nJoshua Ojih  \nBachelor of Engineering  \nUniversity of Benin, Benin City, Nigeria, 2015  \nMaster of Science in Mechanical Engineering  \nUniversity of South Carolina, 2021  \nSubmitted in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Mechanical Engineering  \nMolinaroli College of Engineering and Computing University of South Carolina 2024  \nAccepted by: Ming Hu, Major Professor Dongkyu Lee, Committee Member Chen Li, Committee Member Jianjun Hu, Committee Member Ann Vail, Dean of the Graduate School  \n© Copyright by Joshua Ojih, 2024 All Rights Reserved.  \nDedication  \nTo God almighty for his endless mercies and provisions for me, all knowledge and understanding concerning this Dissertation comes from God. I would also like to thank my parents and siblings for their love and encouragement in completing this research.  \nAcknowledgments  \nFirst and foremost, I sincerely thank my advisor, Dr. Ming Hu, for his constructive criticism and direction throughout my Dissertation. I would also love to thank other research team members for their support in completing the research.  \nAbstract  \nThe quest for materials with extraordinary properties has been a longstanding endeavor in material science and engineering, driving future technological advancement. However, the discovery of such materials is non-trivial. Recent advancements in computational methods, particularly the integration of machine learning (ML) techniques with density functional theory (DFT), have opened new avenues for accelerating the discovery of materials with exceptional and extreme properties. This dissertation focuses on developing a synergistic approach and workflow combining ML and DFT to identify materials with properties that are pushed beyond current limits, using lattice thermal conductivity (LTC) as a case study of the workflow.  \nWe begin by applying various machine learning (ML) algorithms, including Random Forest, Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), the Atomistic Line Graph Neural Network (ALIGNN), the Orbital Graph Convolution Neural Network (OGCNN), and the Global Attention Graph Neural Network (deeperGATGNN), to predict and classify materials with low or high lattice thermal conductivity (LTC) . This multi-algorithm approach balances the high accuracy of quantum-level calculations with the computational efficiency of classical methods. The iterative loop of training, evaluation, recommendation, and verification continues until no additional materials are identified for ","cbCaidu149osPqem","https://ap.wps.com/l/cbCaidu149osPqem","pdf",6751507,3,1,178,"English","en",105,"# Dedication\n# Acknowledgments\n# Abstract\n# List of Tables\n# List of Figures\n# Chapter 1: Introduction\n# Chapter 2: Literature Review\n# Chapter 3: Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity\n# Chapter 4: Screening Outstanding Mechanical Properties and Low Lattice Thermal Conductivity using Global Attention Graph Neural Network\n# Chapter 5: High-throughput Computational Discovery of 3216 Ul","[{\"question\":\"What workflow does the dissertation propose for discovering extreme thermal materials?\",\"answer\":\"It combines machine learning with density functional theory in an iterative loop of training, evaluation, recommendation, and verification to identify materials with properties beyond current limits.\"},{\"question\":\"How is lattice thermal conductivity used in the study?\",\"answer\":\"Lattice thermal conductivity (LTC) is the main case study property, with models trained to predict and classify materials as low or high LTC to guide screening and selection.\"},{\"question\":\"Why does the dissertation expand the candidate crystal structure pool, and how is it done?\",\"answer\":\"It addresses limitations of screening only from existing databases by generating new dynamically stable structures using MAGUS together with learned physical and chemical features.\"}]","Data-Driven Discovery of Extreme Thermal Materials by High-Throughput Computation and Machine Learning | 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