[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124791-en":3,"doc-seo-124791-105":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},124791,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Atomistic Insights Into Material Chemistry - From First Principles to Machine Learning - Dissertation","Atomistic Insights Into Material Chemistry: From First Principles to Machine Learning presents an ab-initio and machine-learning framework for understanding chemical reactions and designing new materials under pressures from water pollution and energy scarcity. Ab-initio simulations enable analysis of charge transfer, chemical stability, electron/hole conduction, and reaction energetics, yet their high computational cost limits large, complex systems in density functional theory workflows. Using DFT-generated datasets to train ML models, the dissertation accelerates prediction of chemical properties and reaction dynamics, combining first-principles exploration and ML-assisted structure–property analysis.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nAtomistic Insights Into Material Chemistry: From First Principles to Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/71w2c6fn](https://escholarship.org/uc/item/71w2c6fn)  \nAuthor  \nKwon, Hyuna  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nAtomistic Insights Into Material Chemistry: From First Principles to Machine Learning  \nA Dissertation submitted in partial satisfaction  \nof the requirements for the degree of  \nDoctor of Philosophy  \nin  \nChemical & Environmental Engineering  \nby  \nHyuna Kwon  \nMarch 2023  \nDissertation Committee:  \nDr. De-en Jiang, Co-Chairperson  \nDr. Bryan M. Wong, Co-Chairperson  \nDr. Juchen Guo  \nCopyright by Hyuna Kwon 2023  \nThe Dissertation of Hyuna Kwon is approved:  \nCommittee Co-Chairperson  \nCommittee Co-Chairperson  \nUniversity of California, Riverside  \nCOPYRIGHT ACKNOWLEDGEMENT  \nThe text and figures in Chapter 3, in part or full, are reproduced from “Tuning Metal– Dihydrogen Interaction in Metal–Organic Frameworks for Hydrogen Storage,” J. Phys. Chem. Lett. 2022, 13 (39), 9129-9133. The coauthor (Dr. De-en Jiang) directed and supervised this research.  \nThe text and figures in Chapter 4, in part or full, are reproduced from “Electron/Hole Mobilities of Periodic DNA and Nucleobases Structures from Large-Scale DFT Calculations,” under review. The coauthor (Dr. Bryan M. Wong) directed and supervised this research.  \nThe text and figures in Chapter 5, in part or full, are reproduced from “Understanding Electrooxidation of Furfural on Cu, Co-Spinel Oxides from Density Functional Theory”(In preparation). The coauthor (Dr. De-en Jiang) directed and supervised this research. The text and figures in Chapter 6, in part or full, are reproduced from “Harnessing SemiSupervised Machine Learning to Automatically Predict Bioactivities of Per-and Polyfluoroalkyl Substances (PFASs) .” Environ. Sci. Tech. Lett. 2022, [https://doi.org/10.1021/acs.estlett.2c00530. The](https://doi.org/10.1021/acs.estlett.2c00530. The) coauthor (Dr. Bryan M. Wong) directed and supervised this research.  \nThe text and figures in Chapter 7, in part or full, are reproduced from “Harnessing Neural Network for Predicting XANES Spectroscopy of Amorphous Carbon Materials”(In preparation). The coauthor (Dr. Tuan Anh Pham) directed and supervised this research.  \nACKNOWLEDGEMENTS  \nFirst, I would like to express my deepest gratitude to my advisors, Dr. De-en Jiang and Dr. Bryan M. Wong, for their unwavering support and guidance throughout my Ph.D. journey. I have learned invaluable lessons about conducting research, from asking scientific questions, providing quantitative evidence for a hypothesis, and effectively presenting the work to the public. My research will be continuously inspired by the knowledge I gained from them.  \nI would also like to thank Dr. Tuan Anh Pham at Lawrence Livermore National Laboratory (LLNL), who offered me a precious summer internship supported by Computational Chemistry and Materials Science program. With his help and encouragement, I had a life-changing opportunity to participate in exciting projects and collaborate with great researchers at LLNL.  \nI thank my dissertation committee member, Dr. Juchen Guo, for his guidance. In addition, I want to thank my candidacy exam committee members, Dr. Jianzhong Wu and Dr. Gregory Beran, for their courteous service.  \nI am also grateful to my colleagues at UC Riverside, Dr. Lu Wang, Dr. Tao Wu, Dr. Kristen Wang Romero, Dr. Tongyu Liu, Ms. Yuqing Fu, Mr. Chuanye Xiong, Mr. Yujing Tong, Mr. Haohong Song, and Ms. Qiuyao Li for their insightful discussion and collaboration.  \nLastly, I thank my family and friends for their encouragement throughout my Ph.D. journey. Their love and understanding have been a constant sourc","cbCair9L5T4U9en9","https://ap.wps.com/l/cbCair9L5T4U9en9","pdf",5842959,1,168,"English","en",105,"# Abstract of the Dissertation\n## Core Motivation and Approach\n## Part I: First-Principles Exploration\n## Part II: Machine-Learning-Assisted Acceleration","[{\"question\":\"Why does the dissertation use both first-principles simulations and machine learning?\",\"answer\":\"First-principles simulations reveal structural and chemical properties such as charge transfer, stability, and reaction energetics. Machine learning is introduced to accelerate predictions because full ab-initio calculations become too costly for large, complex systems.\"},{\"question\":\"What kinds of properties and processes are analyzed using ab-initio simulations?\",\"answer\":\"The dissertation focuses on structure–property relationships and electronic structure, including conduction of electrons/holes and reaction energetics in chemical materials and interfaces.\"},{\"question\":\"What research topics are covered in the two-part dissertation structure?\",\"answer\":\"Part I uses first principles to study structure–property relationships and reaction energetics for systems such as metal-organic frameworks, DNA conductivity, and biomass electrooxidation. Part II develops machine-learning-assisted methods to speed up investigations of structure–property relationships.\"}]","Atomistic Insights Into Material Chemistry - From First Principles to Machine Learning - Dissertation | PDF",1785894674,423,{"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},"atomistic-insights-into-material-chemistry-from-first-principles-to-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/atomistic-insights-into-material-chemistry-from-first-principles-to-machine-learning-dissertation/124791/",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 does the dissertation use both first-principles simulations and machine learning?","Question",{"text":75,"@type":76},"First-principles simulations reveal structural and chemical properties such as charge transfer, stability, and reaction energetics. Machine learning is introduced to accelerate predictions because full ab-initio calculations become too costly for large, complex systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of properties and processes are analyzed using ab-initio simulations?",{"text":80,"@type":76},"The dissertation focuses on structure–property relationships and electronic structure, including conduction of electrons/holes and reaction energetics in chemical materials and interfaces.",{"name":82,"@type":73,"acceptedAnswer":83},"What research topics are covered in the two-part dissertation structure?",{"text":84,"@type":76},"Part I uses first principles to study structure–property relationships and reaction energetics for systems such as metal-organic frameworks, DNA conductivity, and biomass electrooxidation. Part II develops machine-learning-assisted methods to speed up investigations of structure–property relationships.","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"]