[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123465-en":3,"doc-seo-123465-105":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},123465,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning and reaction dynamics - From spectroscopic constants of diatomic molecules to buffer gas experiments - Summary","This thesis investigates spectroscopic properties and chemistry of diatomic molecules with strong potential for quantum information and ultracold chemistry. A Diatomic Molecular Spectroscopy Database was implemented to consolidate spectroscopic data and enable computation and visualization of Franck-Condon factors, with support for user contributions. Machine learning models reveal relationships among spectroscopic constants and predict experimental electric dipole moments from these constants. The work further evaluates quantum-chemical methods using CCSD(T) for dipole moments, computes hyperfine constants of AlF for comparison, and studies laser cooling candidates AlF and CaF produced in a buffer gas cell, including machine-learning fitting of an AlF-AlF potential energy surface.","Erstgutachter/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 Verwendung dieses Datensatzes wurde gezeigt, dass ein Machine-Learning-Modell Dipolmomente genau vorhersagen kann, indem es spektroskopische Konstanten verwendet.  \nDie Verfügbarkeit präziser spektroskopischer Daten ermöglicht eine gründliche Bewertung fortschrittlicher quantenchemischer Methoden. Insbesondere untersuchten wir die Genauigkeit des gekoppelten Clusteransatzes mit einfachen, doppelten und perturbativen dreifachen Anregungen (CCSD(T)) bei der Vorhersage von elektrischen Dipolmomenten in Kombination mit verschiedenen Basissätzen. Zusätzlich wurden die Hyperfeinstrukturkonstanten für den a3Π-Zustand von Aluminiummonofluorid (AlF) berechnet und mit experimentellen Werten verglichen. Unsere Studie betont die Bedeutung einer gründlichen Bewertung, die sowohl experimentelle als auch theoretische Methoden umfass","cbCaimN6cOcGNtGd","https://ap.wps.com/l/cbCaimN6cOcGNtGd","pdf",44526536,1,259,"English","en",105,"# Summary\n## Diatomic Molecular Spectroscopy Database\n## Machine learning for spectroscopic constants and dipole moments\n## Quantum chemistry evaluation (CCSD(T), dipole moments, hyperfine constants)\n## Buffer gas experiments and potential energy surface fitting","[{\"question\":\"What database was implemented in the thesis, and what functions does it provide?\",\"answer\":\"A Diatomic Molecular Spectroscopy Database was implemented via a dynamic website. It consolidates spectroscopic information and enables calculation and visualization of Franck-Condon factors, with flexibility for user contributions.\"},{\"question\":\"How do the machine learning models connect spectroscopic constants to physical observables?\",\"answer\":\"Machine learning models use input features based on the group and period of the constituent atoms to reveal relationships among spectroscopic constants. Using a dataset of experimental electric dipole moments, the models can accurately predict dipole moments from spectroscopic constants.\"},{\"question\":\"Which quantum-chemistry methods and comparisons were performed for dipole moments and hyperfine structure?\",\"answer\":\"The thesis assessed the accuracy of CCSD(T) for predicting electric dipole moments across different basis sets. It also computed hyperfine constants for the a3Π state of AlF and compared them with experimental values.\"}]","Machine learning and reaction dynamics - From spectroscopic constants of diatomic molecules to buffer gas experiments - Summary | PDF",1785816673,653,{"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-experiments-summary","",{"@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-experiments-summary/123465/",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 database was implemented in the thesis, and what functions does it provide?","Question",{"text":75,"@type":76},"A Diatomic Molecular Spectroscopy Database was implemented via a dynamic website. It consolidates spectroscopic information and enables calculation and visualization of Franck-Condon factors, with flexibility for user contributions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the machine learning models connect spectroscopic constants to physical observables?",{"text":80,"@type":76},"Machine learning models use input features based on the group and period of the constituent atoms to reveal relationships among spectroscopic constants. Using a dataset of experimental electric dipole moments, the models can accurately predict dipole moments from spectroscopic constants.",{"name":82,"@type":73,"acceptedAnswer":83},"Which quantum-chemistry methods and comparisons were performed for dipole moments and hyperfine structure?",{"text":84,"@type":76},"The thesis assessed the accuracy of CCSD(T) for predicting electric dipole moments across different basis sets. It also computed hyperfine constants for the a3Π state of AlF and compared them 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"]