[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121934-en":3,"doc-seo-121934-105":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},121934,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Geometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization","Geometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization presents a dissertation in computational chemistry structured around algorithms first and applications second. The work builds software and machine-learning workflows to analyze quantum descriptors and heterogeneous, dynamic fields relevant to enzymology, with emphasis on QTAIM-based decomposition from molecular densities. It demonstrates prediction of Diels–Alder reaction barriers from QTAIM signatures, high-throughput QTAIM computation for geometric deep learning, and an electric-field framework tied to electrostatic preorganization. Later chapters extend clustering and field tracking to explain directed evolution in protein trajectories and identify structures for QM/MM studies.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nGeometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization  \nPermalink  \n[https://escholarship.org/uc/item/15h6w916](https://escholarship.org/uc/item/15h6w916)  \nAuthor  \nVargas, Santiago  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nGeometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic  \nPreorganization  \nA dissertation submitted in partial satisfaction  \nof the requirements for the degree  \nDoctor of Philosophy in Chemistry  \nby  \nSantiago Vargas  \n© Copyright by Santiago Vargas 2024  \nABSTRACT OF THE DISSERTATION  \nGeometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic  \nPreorganization  \nby  \nSantiago Vargas  \nDoctor of Philosophy in Chemistry  \nUniversity of California, Los Angeles, 2024  \nAnastassia N. Alexandrova, Chair  \nThis thesis is organized in a slightly unconventional fashion: algorithms lead and applications fill out the content. I think this emphasizes my interests during graduate schoolI built algorithms and tools to address issues that were otherwise inaccessible to different areas of computational chemistry (including applied machine learning) and enzymology. Two sets of scientific thrusts underscore the bulk of my work: algorithms to analyze dynamic, heterogeneous fields in the context of enzymology and flexible machine learning algorithms, including those that leverage quantum descriptors, for rigorous molecular and reaction-level properties. Each section will include grounding on applications and broader impacts for the reader as well. Now we pivot to discussing the main thrusts and outlining each chapter briefly.  \nGeneral ML and Quantum Theory of Atoms-in-Molecules (QTAIM): QTAIM serves as a mathematical decomposition algorithm for electronic basins within a molecule. The algorithm intakes molecular densities, as computed (typically) by density functional theory (DFT), and uses the flux of density to partition the scalar field into 3-dimensional atomic basins of density [14 , 16] . These objects are known as atomic basins and represent the quantum atom within a molecule. By constructing these structures, we compute a rich set of mathematical descriptors that map to many features including energies, bonding,  \nand electron delocalization. These features have been correlated, in the past, to activation energies, reactivity, and overall system energies, but these uses largely relied on human intervention and small datasets [44 , 62 , 65 , 111 , 142 , 287] . By developing software centered around high-throughput QTAIM calculations and machine learning, I was able to bring these descriptors to larger datasets and a wide host of applications.  \nIn Chapter 2, I discuss an algorithm I implemented to predict Diels-Alder reaction barriers from QTAIM signatures alone. In this study, we showed that QTAIM features, can be used to surmise reaction barriers while also using machine learning techniques to understand what signatures were most informative to our models. Here QTAIM electrostatic potentialsand delocalization indices alone were able to yield great performance on withheld datasets. In addition, we demonstrated that QTAIM features can allow a machine learning model to generalize, to an extent, to much larger Diels-Alder reactions. This chapter was adapted from the following: Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density. S. Vargas*, M. Hannefarth, Z. Liu, A.N. Alexandrova. Journal of Chemical Theory and Computation 2021 17 (10), 6203-6213 . 10.1021/acs.jctc.1c00623 .  \nIn Chapter 3, I discuss a package developed to perform high-throughput QTAIM calculations on datasets of molecules and reactions. This package is currently adap","cbCaivswcAiSZYYu","https://ap.wps.com/l/cbCaivswcAiSZYYu","pdf",30963391,1,265,"English","en",105,"# Abstract\n## General ML and Quantum Theory of Atoms-in-Molecules (QTAIM)\n## Chapter 2: Predicting Diels–Alder reaction barriers from QTAIM signatures\n## Chapter 3: High-throughput QTAIM package and geometric deep learning\n## Advancing analysis of electric fields in proteins\n## Chapter 4: Electric-field analysis for directed evolution trajectories","[{\"question\":\"What is the role of QTAIM in the dissertation?\",\"answer\":\"QTAIM provides a mathematical decomposition algorithm that partitions molecular electron density into 3D atomic basins. From these basins, the work derives descriptors linked to energies, bonding, and electron delocalization.\"},{\"question\":\"How does the dissertation predict Diels–Alder reaction barriers?\",\"answer\":\"Chapter 2 shows that QTAIM electrostatic potentials and delocalization indices can predict Diels–Alder barriers on withheld datasets. It also examines which signatures are most informative for the machine-learning models and how generalization improves for larger reactions.\"},{\"question\":\"What contributions are presented for analyzing electric fields in proteins?\",\"answer\":\"Later chapters develop algorithms to ingest, interpret, and predict electric fields in protein active sites under the concept of electrostatic preorganization. Chapter 4 applies heterogeneous electric-field analysis and clustering along a protoglobin directed evolution trajectory to identify representative structures for QM/MM calculations.\"}]","Geometric Learning for Quantum-Informed, Machine Learning and Analysis of Electrostatic Preorganization | PDF",1785807816,668,{"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},"geometric-learning-for-quantum-informed-machine-learning-and-analysis-of-electrostatic-preorganization","",{"@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/geometric-learning-for-quantum-informed-machine-learning-and-analysis-of-electrostatic-preorganization/121934/",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","2026-08-04",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 is the role of QTAIM in the dissertation?","Question",{"text":75,"@type":76},"QTAIM provides a mathematical decomposition algorithm that partitions molecular electron density into 3D atomic basins. From these basins, the work derives descriptors linked to energies, bonding, and electron delocalization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation predict Diels–Alder reaction barriers?",{"text":80,"@type":76},"Chapter 2 shows that QTAIM electrostatic potentials and delocalization indices can predict Diels–Alder barriers on withheld datasets. It also examines which signatures are most informative for the machine-learning models and how generalization improves for larger reactions.",{"name":82,"@type":73,"acceptedAnswer":83},"What contributions are presented for analyzing electric fields in proteins?",{"text":84,"@type":76},"Later chapters develop algorithms to ingest, interpret, and predict electric fields in protein active sites under the concept of electrostatic preorganization. Chapter 4 applies heterogeneous electric-field analysis and clustering along a protoglobin directed evolution trajectory to identify representative structures for QM/MM calculations.","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"]