[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122515-en":3,"doc-seo-122515-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122515,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","OBTAINING PHYSICAL INSIGHTS FOR DIFFUSION THROUGH MACHINE LEARNING FOR RENEWABLE ENERGY STORAGE APPLICATIONS","Hydrogen storage, oxide fuel cells, and Li-ion batteries support renewable energy storage and transport, yet discovering materials with ideal transport properties remains challenging. Analytical approaches often rely on hand-selected features, limiting quantitative accuracy, while machine learning can predict well but hides which features control diffusion behavior. This dissertation builds interpretable machine learning models and databases to predict activation energies for hydrogen diffusion in metals and random binary alloys. A feature grouping framework identifies packing factor and electronic specific heat as key drivers, then extends to oxygen diffusion in perovskites and pyrochlores, using experimental data and reduced feature sets to enable rapid screening and insight.","© 2025 Grace M. Lu  \nOBTAINING PHYSICAL INSIGHTS FOR DIFFUSION THROUGH MACHINE LEARNING FOR RENEWABLE ENERGY STORAGE APPLICATIONS  \nBY  \nGRACE M. LU  \nDISSERTATION  \nSubmitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Materials Science and Engineering  \nin the Graduate College of the University of Illinois Urbana-Champaign, 2025  \nUrbana, Illinois  \nDoctoral Committee:  \nProfessor Dallas R. Trinkle, Chair and Director of Research  \nProfessor Pascal Bellon  \nProfessor Elif Ertekin  \nProfessor Andre Schleife  \nAbstract  \nHydrogen storage, oxide fuel cells, and Li-ion batteries are three techniques that enable renewable energy storage and transport. To improve their performance, new materials need to be discovered with ideal transport properties. To explore the large material space efficiently, machine learning methods provide physical insightsand enable the rapid screening of new materials. While analytic models use hand-selected features that have clear physical ties, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which features are important. Additionally, machine learned interatomic potentials can allow near-DFT levels of accuracy on large systems for which DFT calculations would be prohibitively expensive.  \nTo develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database and fit six machine learning models. Grouped feature importances, formed by combining the features via their correlations, reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.  \nWe then expand this framework by showing its applicability to predicting transport properties in more complex materials and as a feature down-selection method. For predicting oxygen diffusion in perovskitesand pyrochlores, we build a database of experimental activation energies and use our grouping framework to reduce the number of material property features. These features are then used to fit seven different machine learning models. An ensemble consensus determines that the most important features for predicting the activation energy are the ionicity of the A-site bond and the partial pressure of oxygen for perovskites. For pyrochlores, the two most important features are the A-site s valence electron count and the B-site electronegativity. The most important features are all constructed using the weighted averages of elemental metal properties, despite weighted averages of the constituent binary oxides being included in our feature set. This is surprising because the material properties of the constituent oxides are more similar to the experimentally measured properties of perovskites and pyrochlores than the features of the metals that are chosen.  \nInclusion of Ag at the electrolyte-anode interface has been shown to reduce dendrite growth, and enable the use of anode-free lithium-ion batteries through an alloying process [1, 2] . Using the pre-trained MACE-MP-0 potential, we explore interfacial structures between FCC Ag and Li, and conclude that the FCC Li phase is more energetically favorable. We demonstrate that there are negligible migration barriers for Li atoms to migrate across the interface into the Ag. However, larger migration barriers for diffusion of vacancies from the interface into the Li slab can impede the mixing process kinetically.  \nAcknowledgments  \nI would like to extend the deepest gratitude for my advisor Professor Dallas R. Trinkle for his academic guidance, support, and unwavering trust. From hi","cbCaiv3GcdmESEeJ","https://ap.wps.com/l/cbCaiv3GcdmESEeJ","pdf",7450236,1,101,"English","en",105,"# Abstract\n## Interpretable machine learning for hydrogen diffusion\n## Feature grouping framework and rapid material screening\n## Extension to oxygen diffusion in perovskites and pyrochlores\n## Interface diffusion insights for Ag-FCC Li systems\n## Acknowledgments","[{\"question\":\"Why are interpretable machine learning models needed for diffusion studies in renewable energy materials?\",\"answer\":\"Machine learning can achieve accurate predictions, but its internal mechanisms are often opaque, making it unclear which physical features govern diffusion and activation energies. Interpretable frameworks identify feature contributions tied to transport behavior.\"},{\"question\":\"How does the dissertation make hydrogen diffusion predictions in metals and random binary alloys?\",\"answer\":\"It creates a database and fits six machine learning models to activation energy targets. Grouped feature importances, formed by combining correlated features, highlight the packing factor and electronic specific heat as particularly significant.\"},{\"question\":\"How is the framework extended to oxygen diffusion in perovskites and pyrochlores?\",\"answer\":\"The work builds a dataset of experimental activation energies and uses the grouping framework to down-select material-property features. The most important features are determined via ensemble consensus for each material family, enabling efficient model fitting with fewer inputs.\"}]","OBTAINING PHYSICAL INSIGHTS FOR DIFFUSION THROUGH MACHINE LEARNING FOR RENEWABLE ENERGY STORAGE APPLICATIONS | PDF",1785811040,255,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"obtaining-physical-insights-for-diffusion-through-machine-learning-for-renewable-energy-storage-applications","",{"@graph":36,"@context":86},[37,54,69],{"@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/obtaining-physical-insights-for-diffusion-through-machine-learning-for-renewable-energy-storage-applications/122515/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are interpretable machine learning models needed for diffusion studies in renewable energy materials?","Question",{"text":76,"@type":77},"Machine learning can achieve accurate predictions, but its internal mechanisms are often opaque, making it unclear which physical features govern diffusion and activation energies. Interpretable frameworks identify feature contributions tied to transport behavior.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the dissertation make hydrogen diffusion predictions in metals and random binary alloys?",{"text":81,"@type":77},"It creates a database and fits six machine learning models to activation energy targets. Grouped feature importances, formed by combining correlated features, highlight the packing factor and electronic specific heat as particularly significant.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the framework extended to oxygen diffusion in perovskites and pyrochlores?",{"text":85,"@type":77},"The work builds a dataset of experimental activation energies and uses the grouping framework to down-select material-property features. 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