[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125515-en":3,"doc-seo-125515-105":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},125515,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning for Supported Organic Electrode Materials - Dissertation","The transition from metal-based cathodes to sustainable, earth-abundant alternatives is limited by poor electronic conductivity and high electrolyte solubility of many organic redox materials. This dissertation develops an integrated quantum-chemical and machine-learning framework enabling a fast, data-driven path from molecular concepts to high-performance organic cathodes. It benchmarks DFT, builds the ReSolved dataset (~19,000 molecules), and uses solvent-aware graph networks for reduction potentials and inverse design. It further introduces deCOFpose for COF fragmentation and models composite electrode adsorption on graphene, reducing screening time for viable cathodes.","Dissertation  \nsubmitted to the combined  \nFaculty of Mathematics, Engineering and Natural Sciences Ruprecht-Karls-Universit¨at Heidelberg  \nfor the degree of  \nDoctor of Natural Sciences (Dr. rer. nat.)  \nMachine Learning for Supported Organic  \nElectrode Materials  \nPut forward by  \nRostislav Fedorov  \nReferees:  \nProf. Dr. Frauke Gr¨ater  \nA/Prof. Dr. Ganna (Anya) Gryn’ova  \nMachine Learning for Supported Organic  \nElectrode Materials  \nOral examination: 24.09.2025  \nAbstract  \nThe transition from metal–based cathodes to sustainable, earth-abundant alternatives is constrained by the poor electronic conductivity and high electrolyte solubility of most organic redox materials. This thesis develops an integrated quantum-chemical and machine-learning framework that overcomes these barriers and delivers a fast, data-driven route from molecular concepts to high-performance organic cathodes.  \nFirst, a density-functional-theory (DFT) workflow was benchmarked against experimental data and used to create ReSolved, a curated dataset of approximately 19 000 closed-and open-shell organic molecules in five solvents. A graph neural network with a solvent-aware set-transformer read-out reproduces absolute reduction potentials with a mean absolute error of →0 .20 eV and transfers accurately to unseen solvents. Coupled to an evolutionary algorithm, the model enables inverse design, rapidly proposing synthetically accessible molecules tailored to specific battery chemistries.  \nTo extend molecular insights to extended solids, the thesis introduces deCOFpose: an automated, topologically aware fragmentation algorithm that dissects crystalline covalent organic frameworks (COFs) into chemically meaningful nodes and linkers. Applied to the CoRE-COF database, deCOFpose successfully processes >70 % of structures, reveals systematic trends between fragment properties and COF band gaps, and feeds a modified PORMAKE builder that can enumerate millions of plausible frameworks for high-throughput screening.  \nAddressing composite electrodes, a semi-empirical tight-binding protocol was combined with a symmetry-adapted, P6mm wallpaper-group equivariant graph neural network (WallpaperNet) to predict adsorption geometries, binding energies, and force fields for small molecules on graphene.  \nTogether, these contributions introduce a set of tools that span length scales from single molecules to periodic frameworks and composite materials, significantly reducing the screening time for viable organic cathodes. Beyond batteries, the methodologies, datasets, algorithms, and software are broadly applicable to catalysis and molecular sensing, furnishing a versatile platform for the accelerated discovery of sustainable, carbon-based functional materials.  \nZusammenfassung  \nDer Wechsel von metallbasierten zu organischen Kathodenmaterialien, die eine bessere Verfu¨gbarkeit und Nachhaltigkeit versprechen, wird bislang durch zwei zentrale Herausforderungen gebremst: die geringe elektrische Leitfa¨higkeit und die hohe Lo¨slichkeitvieler organischer Redoxmaterialien im Elektrolyten. Diese Arbeit stellt ein integriertes Framework vor, das Quantenchemie mit maschinellem Lernen kombiniert, um genau diese Barrieren zu u¨berwinden.  \nZuna¨chst wurde ein Workflow auf Basis der Dichtefunktionaltheorie (DFT) gegen experimentelle Daten validiert und genutzt, um ReSolvedDB zu erstellen-einen kuratierten Datensatz von etwa 19.000 organischen Moleku¨len mit geschlossenen undo!enen Elektronensystemen in fu¨nf Lo¨sungsmitteln. Ein graph-neuronales Netzwerksagt absolute Reduktionspotenziale in verschiedenen Lo¨sungsmitteln mit einer mittleren absoluten Abweichung von ca. 0,20 eV vorher und la¨sst sich pra¨zise auf unbekannte Lo¨sungsmittel u¨bertragen. In Kombination mit einem Evolutionsalgorithmusermo¨glicht das Modell ein inverses Design: Es schla¨gt schnell synthetisch zuga¨ngliche Moleku¨le vor, die gezielt auf unterschiedliche Batteriematerialien zugeschnitten sind.  \nUm molekulare Erke","cbCaittBW9Fj8QuD","https://ap.wps.com/l/cbCaittBW9Fj8QuD","pdf",48534831,1,202,"English","en",105,"# Abstract\n## Quantum-chemical benchmarking and ReSolved dataset\n## Solvent-aware graph neural networks and inverse design\n## deCOFpose for crystalline COFs and high-throughput enumeration\n## WallpaperNet for composite electrodes on graphene\n## Broader applicability and impact","[{\"question\":\"What are the main challenges addressed by the thesis?\",\"answer\":\"Organic redox cathodes face poor electronic conductivity and high solubility in electrolytes, which restrict practical performance. The thesis targets both barriers with an integrated modeling framework.\"},{\"question\":\"How does the thesis build and use the ReSolved dataset?\",\"answer\":\"It benchmarks a DFT workflow against experimental data, then creates ReSolved, a curated dataset of about 19,000 organic molecules in five solvents. A solvent-aware graph neural network reproduces absolute reduction potentials and transfers to unseen solvents.\"},{\"question\":\"How does deCOFpose support extending molecular insights to solids?\",\"answer\":\"deCOFpose is an automated, topologically informed fragmentation algorithm that breaks crystalline COFs into chemically meaningful nodes and linkers. Applied to the CoRE-COF database, it processes over 70% of structures and enables enumeration of millions of frameworks for screening.\"}]","Machine Learning for Supported Organic Electrode Materials - Dissertation | PDF",1785899553,509,{"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-for-supported-organic-electrode-materials-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-for-supported-organic-electrode-materials-dissertation/125515/",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},"What are the main challenges addressed by the thesis?","Question",{"text":75,"@type":76},"Organic redox cathodes face poor electronic conductivity and high solubility in electrolytes, which restrict practical performance. The thesis targets both barriers with an integrated modeling framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis build and use the ReSolved dataset?",{"text":80,"@type":76},"It benchmarks a DFT workflow against experimental data, then creates ReSolved, a curated dataset of about 19,000 organic molecules in five solvents. A solvent-aware graph neural network reproduces absolute reduction potentials and transfers to unseen solvents.",{"name":82,"@type":73,"acceptedAnswer":83},"How does deCOFpose support extending molecular insights to solids?",{"text":84,"@type":76},"deCOFpose is an automated, topologically informed fragmentation algorithm that breaks crystalline COFs into chemically meaningful nodes and linkers. Applied to the CoRE-COF database, it processes over 70% of structures and enables enumeration of millions of frameworks for screening.","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"]