[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117183-en":3,"doc-seo-117183-105":30,"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":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},117183,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning-aided Chemical Kinetic Modeling","Machine Learning-aided Chemical Kinetic Modeling advances chemical kinetic modeling by improving the ability to predict and interpret the molecular events occurring in complex reactions. The work addresses the difficulty of constructing models for real systems, where chemical space is vast and the computational cost is driven by system scale and the need for interatomic potentials with first-principles accuracy. It leverages machine learning to extend feasible system and time scales, using machine-learning interatomic potentials and data-driven approaches to balance efficiency with chemical interpretability.","Copyright by  \nJiyoung Lee 2024  \n1  \nThe Dissertation Committee for Jiyoung Lee certi􀀌es that this is the approved version of the following dissertation:  \nMachine Learning-aided Chemical Kinetic Modeling  \nCommittee:  \nGraeme Henkelman, Supervisor Danny Perez, Co-supervisor Feliciano Giustino  \nRobert Moser Irene Gamba Donald Siegel Hang Ren  \nMachine Learning-aided Chemical Kinetic Modeling  \nby  \nJiyoung Lee  \nDissertation  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Ful􀀌llment of the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin May 2024  \nAcknowledgments  \nFirst and the foremost, I want to thank God my Lord, who has been the light on my way. Without Him, I would have not been able to come to Austin and go through all processes. I would like to thank my adviser Prof. Graeme Henkelman for guiding me for the last 􀀌ve years with generous supports for research and traveling to di􀀋erent cities. Without his support, I could have not graduated in 􀀌ve years. I also want to thank Dr. Ping Yang and Dr. Danny Perez, who advise my last Ph.D year at Los Alamos National Laboratory with patience and supports. I would like to acknowledge Prof. Feliciano Giustino, Prof. Robert Moser, Prof. Irene Gamba, Prof. Donald Siegel, and Prof. Hang Ren for serving as members of my dissertation committee dedicating their valuable time. I also would like to thank my another mentor Dr. Sina Mostafanejad for listening to all of vents from a frustrated graudate student and giving me advices for my research. I thank my collaborators, Prof. Lei Li and his group members, Dr. Ryan Ciufo, Dr. Naman Katyal, Prof. Wissam Saidi, Mai Nguyen, Richard Garza, Dr. Logan Augustine, Dr. Michael Taylor, Dr. Yufei Wang, Victor Karamalis, Zafer Acar, and Prof. Joshua Schrier.  \nI cannot thank enough to Henkelmen group. Our trips to get lunches, discussions during lunch time, and game nights on Friday were all such a joy and full of laughs, always giving me just smile as I think back. Every single one of you meant a lot to me, and I will never forget anyone of you. I wish the best of you all for not only your academic journey, but also your life. Above all, some of you guys led me to 􀀌nd love toward college football, that is extended to national football league now. I also want to thank my group members at Los Alamos National Laboratory for giving me precious feedbacks on my research and my defense talk.  \nI still remember the 􀀌rst day when I came to Austin. It was hot, humid, and endless 􀀌ghts against ants living in my apartment. I did not think that I would love the city this much. Austin becomes my hometown in States, making me feel safe and  \nbeing loved. I have met a number of great people who have willingly shared all of hardships and happiness together with genuine love. I would like to thank my best friend Jiseon, who has been taking care of me like a sister and always being my side. I still remember us going to a park during pandemic and having Subway sandwiches together talking about everything. I am really thankful for meeting a person who I can call a sister, not just the best friend. I also thank my another best friend Yujie, who has been my supporter since we have met in 2014, Iowa. We have been going through almost the same process and struggles for the last 10 years. No matter where you are, I will be always supporting you as your best friend. I would like to thank all of members of the graduate students group at my church in Austin, who taught me how to love people and how much God loves people. I also want to thank members at church in Los Alamos and Santa Fe for sharing their own experience in Ph.D and praying for me. Lastly but not the least, I want to thank my family for being my rock with unending supports, love, and prayers.  \nThe works in this dissertation were supported by NSF grant CHE-2102317, a fellowship from the Molecular Science Software Institu","cbCaisJ18LqXKgEV","https://ap.wps.com/l/cbCaisJ18LqXKgEV","pdf",5514393,1,149,"English","en",105,"# Dissertation Metadata\n## Dissertation Committee and Presentation\n## Acknowledgments\n## Funding and Computational Resources\n## Abstract\n# Core Research Theme\n## Motivation and Challenges in Kinetic Modeling\n## Machine Learning Approaches for Scale and Interpretability","[{\"question\":\"What problem does Chemical Kinetic Modeling face in real chemical systems?\",\"answer\":\"Predicting and interpreting underlying chemistry in real systems is difficult because chemical space is vast and the required models are computationally expensive.\"},{\"question\":\"Why are interatomic potentials important for accurate kinetic modeling?\",\"answer\":\"Accurate modeling depends on interatomic potentials that maintain first-principles accuracy for molecular-level processes, which contributes strongly to computational cost.\"},{\"question\":\"How does machine learning help in kinetic modeling according to the document?\",\"answer\":\"Machine learning techniques expand achievable system and time scales, particularly through machine-learning interatomic potentials and data-driven models that improve efficiency while retaining chemical interpretability.\"}]","Machine Learning-aided Chemical Kinetic Modeling | 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problem does Chemical Kinetic Modeling face in real chemical systems?","Question",{"text":76,"@type":77},"Predicting and interpreting underlying chemistry in real systems is difficult because chemical space is vast and the required models are computationally expensive.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why are interatomic potentials important for accurate kinetic modeling?",{"text":81,"@type":77},"Accurate modeling depends on interatomic potentials that maintain first-principles accuracy for molecular-level processes, which contributes strongly to computational cost.",{"name":83,"@type":74,"acceptedAnswer":84},"How does machine learning help in kinetic modeling according to the document?",{"text":85,"@type":77},"Machine learning techniques expand achievable system and time scales, particularly through machine-learning interatomic potentials and data-driven models that improve efficiency while retaining chemical 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