[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122933-en":3,"doc-seo-122933-105":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},122933,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning and reaction dynamics - From spectroscopic constants of diatomic molecules to buffer gas chemistry - Dissertation","This dissertation investigates spectroscopic properties and chemistry of diatomic molecules with strong potential for quantum information and ultracold chemistry. A Diatomic Molecular Spectroscopy Database is implemented to consolidate spectroscopic constants and to compute and visualize Franck–Condon factors, supporting dynamic access and user contributions. Machine learning models use atomic group/period features to uncover relationships among spectroscopic constants and to predict electric dipole moments from a curated dataset. The work also rigorously evaluates quantum-chemical methods via CCSD(T) accuracy across basis sets and compares calculated hyperfine constants for AlF with experiments, and extends applications to laser cooling candidates and ML-driven potential energy surface fitting.","Liu, Xiangyue  \nMachine learning and reaction dynamics: From spectroscopic constants of diatomic molecules to buffer gas chemistry  \nImage reproduced from Digital Discovery, 2024, 3, 34-50, [https://doi.org/10.1039/D3DD00152K](https://doi.org/10.1039/D3DD00152K), with permission from the Royal Society of Chemistry.  \nMachine learning and reaction dynamics:  \nFrom spectroscopic constants of diatomic molecules to buffer gas chemistry  \nDissertation  \nzur Erlangung des Grades eines Doctor rerum naturalium  \n(Dr. rer. nat.)  \nam Fachbereich Physik der Freien Universität Berlin  \nvorgelegt von  \nXiangyue Liu  \nBerlin 2023  \nImage reproduced from Phys. Chem. Chem. Phys., 2020,22, 24191-24200, [https://doi.org/10.1039/D0CP03810E](https://doi.org/10.1039/D0CP03810E), with permission from the Royal Society of Chemistry.  \nErstgutachter/in: Prof. Dr. Gerard Meijer Zweitgutachter/in: Prof. Dr. Piet Brouwer  \nTag der Disputation: 18.01.2024  \nSummary  \nThis thesis explores the spectroscopic properties and chemistry of diatomic molecules, which hold significant promise for applications in areas like quantum information and ultracold chemistry.  \nFirstly, the Diatomic Molecular Spectroscopy Database, accessible through a dynamic website, has been implemented. This database predominantly consolidates spectroscopic information while enabling the computation and visualization of Franck-Condon factors, and is adaptable for user contributions. Based on this database, machine learning models have been built to effectively reveal relationships among spectroscopic constants, with input features based on constituent atoms’ group and period. Similarly, a comprehensive dataset of contemporary experimental electric dipole moments has been created. Utilizing this dataset, it has been shown that a machine learning model can accurately predict dipole moments using spectroscopic constants.  \nThe availability of precise spectroscopic data allows for a rigorous assessment of advanced quantum chemistry methods. Specifically, we investigated the accuracy of coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] in predicting electric dipole moments when combined with different basis sets. Additionally, the hyperfine constants for the a3Π state of aluminum monofluoride (AlF) have been computed and compared to experimental values. Our study underscores the significance of a thorough evaluation encompassing both experimental and theoretical methodologies.  \nAlF and calcium monofluoride (CaF), among other metal monofluorides, have emerged as highly promising options for experiments involving laser cooling and trapping of cold molecules. We have compared the efficiency of different fluorine-donor molecules producing AlF and CaF through metal atom ablation in a buffer gas cell. Additionally, we present an efficient machine learning method for fitting the potential energy surface of AlFAlF system, trained on relevant configurations from molecular dynamics simulations at the CCSD(T) level.  \nZusammenfassung  \nDie vorliegende Arbeit beschäftigt sich mit der Erforschung der spektroskopischen Eigenschaften und Chemie von zweiatomigen Molekülen, die für Anwendungen in Bereichen wie Quanteninformation und ultrakalte Chemie vielversprechend sind.  \nZunächst wurde eine Datenbank zur Spektroskopie zweiatomiger Moleküle implementiert, die über eine dynamische Website zugänglich ist. Diese Datenbank konsolidiert hauptsächlich spektroskopische Informationen undermöglicht gleichzeitig die Berechnung und Visualisierung des FranckCondon-Faktors und ist für Benutzerbeiträge anpassbar.  \nBasierend auf dieser Datenbank wurden Machine-Learning-Modelle entwickelt, um die Beziehungen zwischen den spektroskopischen Konstanten zweiatomiger Moleküle aufzudecken, wobei die Eingabemerkmale auf der Gruppe und der Periode der beteiligten Atome basieren. Ebenso wurde einumfassender Datensatz mit aktuellen experimentellen elektrischen Dipolmomenten erstellt. Unter Verw","cbCaimqpjUiHN5iZ","https://ap.wps.com/l/cbCaimqpjUiHN5iZ","pdf",30365390,1,259,"English","en",105,"# Summary\n## Machine learning for spectroscopic constants and dipole moments\n## Assessment of quantum chemistry methods\n## Cold-molecule experiments and buffer gas cell chemistry\n# Acknowledgement","[{\"question\":\"What databases and datasets are developed in the dissertation?\",\"answer\":\"A Diatomic Molecular Spectroscopy Database is implemented to consolidate spectroscopic information and enable Franck–Condon factor computations and visualization. A comprehensive dataset of contemporary experimental electric dipole moments is also created for model training and validation.\"},{\"question\":\"How are machine learning models used in the work?\",\"answer\":\"Machine learning models are built to reveal relationships among spectroscopic constants using features derived from the constituent atoms’ group and period. The models are also used to accurately predict electric dipole moments from spectroscopic constants.\"},{\"question\":\"Which quantum chemistry methods and properties are evaluated?\",\"answer\":\"The dissertation evaluates CCSD(T) accuracy for predicting electric dipole moments across different basis sets. It also computes and compares hyperfine constants for the a3Π state of AlF with experimental values.\"}]","Machine learning and reaction dynamics - From spectroscopic constants of diatomic molecules to buffer gas chemistry - Dissertation | PDF",1785813765,653,{"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},"machine-learning-and-reaction-dynamics-from-spectroscopic-constants-of-diatomic-molecules-to-buffer-gas-chemistry-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/machine-learning-and-reaction-dynamics-from-spectroscopic-constants-of-diatomic-molecules-to-buffer-gas-chemistry-dissertation/122933/",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 databases and datasets are developed in the dissertation?","Question",{"text":75,"@type":76},"A Diatomic Molecular Spectroscopy Database is implemented to consolidate spectroscopic information and enable Franck–Condon factor computations and visualization. A comprehensive dataset of contemporary experimental electric dipole moments is also created for model training and validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are machine learning models used in the work?",{"text":80,"@type":76},"Machine learning models are built to reveal relationships among spectroscopic constants using features derived from the constituent atoms’ group and period. The models are also used to accurately predict electric dipole moments from spectroscopic constants.",{"name":82,"@type":73,"acceptedAnswer":83},"Which quantum chemistry methods and properties are evaluated?",{"text":84,"@type":76},"The dissertation evaluates CCSD(T) accuracy for predicting electric dipole moments across different basis sets. It also computes and compares hyperfine constants for the a3Π state of AlF with experimental values.","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"]