[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120665-en":3,"doc-seo-120665-105":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},120665,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts","The design of heterogeneous catalysts depends on physically meaningful adsorption energetics and the stable existence of conceived active-site structures on catalyst surfaces. Progress in surface understanding is hindered by the dynamic formation and evolution of interfaces during in-situ reactions. A data-driven machine-learning framework is proposed to search and predict (meta)stable structures, support operando simulation under realistic reaction conditions and micro-environments, and critically interpret experimental characterization data. The review concludes that ML will reduce discovery and design costs.","Minireviews  \n152 13773, 0,  \nCatalyst Design  \nHow to cite:  \nInternational Edition: [doi.org/10.1002/anie.202216383](doi.org/10.1002/anie.202216383)[ ](doi.org/10.1002/anie.202216383)German Edition: [doi.org/10.1002/ange.202216383](doi.org/10.1002/ange.202216383)  \nData-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts  \nHaobo Li, Yan Jiao, Kenneth Davey, and Shi-Zhang Qiao*  \nAngewandte  \n Chemie  \nAngew. Chem. Int. Ed. 2023, e202216383 (1 of 13) © 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH  \nDownloaded from [https://onlinelibrary.wiley.com/doi/10.1002/anie.202216383 by University of Adelaide Alumni, Wiley Online Library on [22/01/2023]. See the Terms and Conditions (](https://onlinelibrary.wiley.com/doi/10.1002/anie.202216383 by University of Adelaide Alumni, Wiley Online Library on [22/01/2023]. See the Terms and Conditions ()[https://onlinelibrary.wiley.com/terms-and-conditions](https://onlinelibrary.wiley.com/terms-and-conditions)) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License  \nMinireviews AngewandteChemie  \n Abstract: The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of  adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with  discovery and design of optimal heterogeneous catalysts.   \n1. Introduction  \nHeterogeneous catalysis is important in the chemical industry, especially in the context of energy conversion, carbon neutrality, and environment protection, and underscores sustainable development. A consensus amongst researchers is that catalytic reactions occur on the surface of heterogeneous catalysts. The surface structure of the catalyst at the gas/solid, or liquid/solid, interface during the catalytic reaction has therefore been subject to extensive investigation to understand reaction mechanisms and design catalyst materials.  \nSurface science began to develop rapidly in the 1970s.[1] Technical approaches to observe catalyst surface structure resulted rapidly in the development of catalysis. With the development of experimental technology, research in electrochemical surface science began in the 1990s.[2] Theoretical developments lagged behind experimental developments, until the advent of quantum chemical computations based on density functional theory (DFT) and application of the generalized gradient approximation (GGA) functional in the mid-1980s.[3] DFT computations are powerfully advantageous in computing the energy of surface structures from first principles and, importantly, to corroborate experiments and characterizations. This combined interdisciplinary fusion of experimental techniques and theoretical computations has become a distinguishing feature of the field.  \nA growing understanding of catalyst surface structures, computational design and screening of catalysts based on the structure of catalytic active sites led to a number of developments beginning in the early 2000s, for example, the work of Nørskov et al. [4] A key question, however, was whether theoretical computations could be relied on to successfully predict experimental outcomes. Surface structu","cbCaivhQkGRJ4joH","https://ap.wps.com/l/cbCaivhQkGRJ4joH","pdf",3072136,1,14,"English","en",105,"# Abstract\n# Introduction\n## Surface-focused catalyst design and challenges\n## Evolution of experimental and theoretical methods\n## Machine learning tools and applications\n# Proposed ML approach and anticipated impact","[{\"question\":\"Why is heterogeneous catalyst design inherently surface-focused?\",\"answer\":\"Because catalytic reactions occur at the gas/solid or liquid/solid interface, where surface structure and adsorption energetics govern activity and mechanism. Stable active-site structures on the surface are prerequisites for meaningful adsorption-energy design.\"},{\"question\":\"What makes understanding catalyst surfaces during reactions difficult in practice?\",\"answer\":\"Dynamic surface formation and evolution under in-situ reaction conditions create complex, time-dependent interfaces. The resulting structural variability increases computational and modeling difficulty for operando simulations.\"},{\"question\":\"How does data-driven machine learning help in studying heterogeneous catalyst surface structures?\",\"answer\":\"ML is used to search and predict (meta)stable structures, assist operando simulations in realistic reaction conditions and micro-environments, and critically analyze experimental characterization data. The review argues this can lower costs for discovering and designing optimal catalysts.\"}]","Data-Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts | PDF",1785731240,35,{"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},"data-driven-machine-learning-for-understanding-surface-structures-of-heterogeneous-catalysts","",{"@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/data-driven-machine-learning-for-understanding-surface-structures-of-heterogeneous-catalysts/120665/",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","2026-08-03",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},"Why is heterogeneous catalyst design inherently surface-focused?","Question",{"text":75,"@type":76},"Because catalytic reactions occur at the gas/solid or liquid/solid interface, where surface structure and adsorption energetics govern activity and mechanism. Stable active-site structures on the surface are prerequisites for meaningful adsorption-energy design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes understanding catalyst surfaces during reactions difficult in practice?",{"text":80,"@type":76},"Dynamic surface formation and evolution under in-situ reaction conditions create complex, time-dependent interfaces. The resulting structural variability increases computational and modeling difficulty for operando simulations.",{"name":82,"@type":73,"acceptedAnswer":83},"How does data-driven machine learning help in studying heterogeneous catalyst surface structures?",{"text":84,"@type":76},"ML is used to search and predict (meta)stable structures, assist operando simulations in realistic reaction conditions and micro-environments, and critically analyze experimental characterization data. 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