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The total C2 yield is compared with O1s XPS characteristics from 51 previously prepared catalysts, and CO32− peak energy is selected as the guiding parameter. Machine learning relates CO32− peak energy to elemental physical descriptors to identify key determinants, then predicts new compositions. Twenty-eight catalysts are synthesized and tested in OCM; Ba-In-Rb/La2O3 emerges as a highly active catalyst with performance comparable to Mn-Na2WO4/SiO2, demonstrating the effectiveness of indirect machine-learning-based catalyst development.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/indirect-design-of-ocm-catalysts-through-machine-learning-of-catalyst-surface-oxygen-species/290765/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/indirect-design-of-ocm-catalysts-through-machine-learning-of-catalyst-surface-oxygen-species/290765.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is catalyst surface oxygen species used as a machine-learning guide for OCM catalyst design?","Question",{"text":113,"@type":114},"OCM catalytic performance is strongly affected by catalyst surface properties, and surface oxygen species are known to impact the reaction. Therefore, learning the surface oxygen feature provides a rational design guide.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the guiding parameter for catalyst design chosen in this work?",{"text":118,"@type":114},"Relationships between total C2 yield and O1s XPS features were evaluated for 51 catalysts. Because only a weak correlation was observed with one feature, the CO32− peak energy was selected as the guiding parameter.",{"name":120,"@type":111,"acceptedAnswer":121},"What was done after the machine-learning model identified important determinants?",{"text":122,"@type":114},"Using the important physical quantities, catalyst compositions were predicted, followed by synthesis of 28 catalysts to verify that their CO32− peak energies fall within a range expected to yield high catalytic performance.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290765,1789642528,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","JAIST Repository  \n[https://dspace.jaist.ac.jp/](https://dspace.jaist.ac.jp/)  \n\n| Title | Indirect Design of OCM Catalysts through Machine Learning of Catalyst Surface Oxygen Species |\n| --- | --- |\n| Author(s) | Nishino, Fumiya; Yoshida, Hiroshi; Machida, Masato; Nishimura, Shun; Takahashi, Keisuke; Ohyama, Junya |\n| Citation | Catalysis Science & Technology, 13(19): 5576-5581 |\n| Issue Date | 2023-08-21 |\n| Type | Journal Article |\n| Text version | author |\n| URL | [http://hdl.handle.net/10119/19337](http://hdl.handle.net/10119/19337) |\n| Rights | Copyright (C) 2023 Royal Society of Chemistry. Fumiya Nishino, Hiroshi Yoshida, Masato Machida, Shun Nishimura, Keisuke Takahashi and Junya Ohyama, Catalysis Science & Technology, 2023, 13(19), 5576-5581. [https://doi.org/10.1039/D3CY00587A](https://doi.org/10.1039/D3CY00587A)Reproduced by permission of the Royal Society of Chemistry |\n| Description |  |\n\nPlease do not adjust margins  \nARTICLE  \nReceived 00th January 20xx, Accepted 00th January 20xx  \nDOI: 10. 1039/x0xx00000x  \nIndirect Design of OCM Catalysts through Machine Learning of Catalyst Surface Oxygen Species  \nFumiya Nishino,a Hiroshi Yoshida,b Masato Machida,c Shun Nishimura,d Keisuke Takahashi *e and Junya Ohyama *c  \nCatalysts for oxidative coupling of methane (OCM) were designed through machine learning of a property of surface oxygen species on the basis of the knowledge that catalytic performance for the OCM is affected by catalyst surface oxygen species. To select the property of the surface oxygen species used as a guide of catalyst design via machine learning, the relationships between the total yield of ethylene and ethane (C2 yield) and the O1s X-ray photoelectron spectral (XPS) features of the 51 catalysts prepared in our previous study were evaluated. Since a weak correlation was seen between the C2 yield and the O1s XPS peak energy of CO32- species on the catalyst surface, the CO32- peak energy was chosen as the guiding parameter of catalyst design in this work. Machine learning was then performed on the dataset consisting of the CO32- peak energy (objective variable) and the physical quantities of elements in the catalysts (descriptor) to find the important physical quantities determining the CO32- peak energy. According to the important physical quantities, catalyst compositions were predicted. Based on the predicted compositions, 28 catalysts were synthesized to verify that their CO32- peak energies were in the range where high catalytic performance can be expected. Furthermore, the catalysts are tested for the OCM reaction. As a result, Ba-In-Rb/La2O3 was found as a new highly active OCM catalyst having compatible activity to the conventional Mn-Na2WO4/SiO2 catalyst. Therefore, it was demonstrated that the indirect catalyst through machine learning of the catalyst surface property is effective for development of catalysts.  \nIntroduction  \nOxidative coupling of methane is a direct conversion reaction of CH4 to ethylene and ethane (C2 compounds), which can offer amore energetically efficient and economical process than the conventional conversion of CH4 to olefin via CO and CH3OH.1 However, the OCM has a problem that the C2 yield hardly exceeds 30% .1, 2 To overcome this problem, hundreds of catalysts have been developed.1-4 Among the catalysts, MnNa2WO4/SiO2 shows relatively high performance for the OCM.5-7 As seen in the case with Mn-Na2WO4/SiO2, combination of elements is effective in developing the OCM catalysts. Here, data science techniques have been applied to search for good combinations of catalyst elements from a vast combinatorial space.3, 4, 8-15 In fact, in our previous studies, catalysts have been  \na. Department of Applied Chemistry and Biochemistry, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto, 860-8555, Japan  \nb. College of Science and Engineering, Kanazawa University, Kakuma-cho, Kanazawa, 920-1192, Japan  \nc. Faculty of Advanced S","cbCaidfuGuOuPkbJ","https://ap.wps.com/l/cbCaidfuGuOuPkbJ","pdf",425680,"English","# Introduction\n## Oxidative coupling of methane and catalyst performance limitations\n## Rationale for combining data science with surface oxygen properties\n# Indirect Design Strategy\n## Selecting the guiding oxygen feature from O1s XPS\n## Machine learning workflow using CO32− peak energy and elemental descriptors\n## Prediction and experimental verification with synthesized catalysts\n# Results and Implications\n## Identification of a new highly active OCM catalyst","[{\"question\":\"Why is catalyst surface oxygen species used as a machine-learning guide for OCM catalyst design?\",\"answer\":\"OCM catalytic performance is strongly affected by catalyst surface properties, and surface oxygen species are known to impact the reaction. Therefore, learning the surface oxygen feature provides a rational design guide.\"},{\"question\":\"How was the guiding parameter for catalyst design chosen in this work?\",\"answer\":\"Relationships between total C2 yield and O1s XPS features were evaluated for 51 catalysts. Because only a weak correlation was observed with one feature, the CO32− peak energy was selected as the guiding parameter.\"},{\"question\":\"What was done after the machine-learning model identified important determinants?\",\"answer\":\"Using the important physical quantities, catalyst compositions were predicted, followed by synthesis of 28 catalysts to verify that their CO32− peak energies fall within a range expected to yield high catalytic performance.\"}]","Indirect Design of OCM Catalysts through Machine Learning of Catalyst Surface Oxygen Species | PDF",18]