[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128711-en":3,"doc-seo-128711-105":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},128711,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-assisted design of metal–organic frameworks for hydrogen storage - A high-throughput screening and experimental approach - read and study","Big data and high-throughput screening methods have accelerated materials discovery, yet translating ML predictions into experimentally realizable compounds remains difficult. This study presents a machine-learning-assisted design strategy for porous metal–organic frameworks (MOFs) aimed at hydrogen storage and targeted synthesizability. Using ML models trained on MOF databases, the approach predicts whether MOF structures are likely feasible to synthesize. Experimental validation includes a newly synthesized vanadium-based MOF showing strong cryogenic H2 storage performance.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nMachine learning-assisted design of metal–organic frameworks for hydrogen storage: A high-throughput screening and experimental approach  \nPermalink  \n[https://escholarship.org/uc/item/1h0372vv](https://escholarship.org/uc/item/1h0372vv)  \nJournal  \nChemical Engineering Journal, 507  \nISSN  \n1385-8947  \nAuthors  \nKim, Wan-Tae  \nLee, Weon-Gyu An, Hong-Eunet al.  \nPublication Date  \n2025-03-01  \nDOI  \n10.1016/j.cej.2025.160766  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n1 Machine Learning-Assisted Design of Metal–Organic Frameworks for Hydrogen  \n2 Storage: A High-Throughput Screening and Experimental Approach  \n3  \n4 Wan-Tae Kim,a,‡ Weon-Gyu Lee,b,‡ Hong-Eun An,a,c Hiroyasu Furukawa,d,e WooSeok Jeong,f,†  \n5 Sung-Chul Kim,g Jeffrey R. Long,d,e,h Sohee Jeong,a,* and Jung-Hoon Leeb,g* 6  \n7 a Materials Architecturing Research Center, Korea Institute of Science and Technology (KIST), 8 Seoul, 02792, Republic of Korea  \n9 bComputational Science Research Center, Korea Institute of Science and Technology (KIST), 10 Seoul, 02792, Republic of Korea  \n11 c Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea  \n12 d Department of Chemistry and Institute for Decarbonization Materials, University of California, Berkeley, 13 California, 94720, United States  \n14 e Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA  \n15 fCenter for AI and Natural Sciences, Korea Institute for Advanced Study (KIAS),  \n16 Seoul, 02455, Republic of Korea  \n17 gAdvanced Analysis Center, Korea Institute of Science and Technology (KIST),  \n18 Seoul, 02792, Republic of Korea  \n19 h Department of Chemical and Biomolecular Engineering and Department of Materials Science and  \n20 Engineering, University of California, Berkeley, California, 94720, USA  \n21 gKU-KIST Graduate School of Converging Science and Technology,  \n22 Korea University, Seoul, 02841, Republic of Korea 23  \n24 *Corresponding author: [soheejeong@kist.re.kr](soheejeong@kist.re.kr) (Sohee Jeong), [jhlee84@kist.re.kr](jhlee84@kist.re.kr) (Jung-Hoon Lee) 25  \n26 †Present address: Computational Science & Engineering Lab. , Korea Institute of Energy Research  \n27 (KIER), Daejeon, 34129, Republic of Korea 28  \n29 ‡Wan-Tae Kim and Weon-Gyu Lee contributed equally to this work.  \n30  \n31 Abstract  \n32 Various theoretical approaches, including big data and high-throughput screening  \n33 techniques, have been explored in developing new materials due to their significant  \n34 potential time-saving advantages. However, it remains a significant challenge to  \n35 experimentally realize new materials that are predicted. In this study, we propose a novel  \n36 materials design strategy that utilizes machine-learning (ML) techniques to predict new  \n37 porous materials that show promise for hydrogen storage and are likely to be feasible to  \n38 synthesize. By leveraging ML techniques and metal−organic framework (MOF) databases, 39 we are able to predict the synthesizability of MOF structures. This is evidenced by the  \n40 successful synthesis of a new vanadium-based MOF that exhibits excellent performance  \n41 for cryogenic H2 storage. Notably, the total gravimetric and volumetric H2 uptakes are as  \n42 high as 9.0 wt % and 50.0 g/L at 77 K and 150 bar. This ML-assisted materials design  \n43 offers an efficient and promising approach for developing hydrogen storage materials. 44  \n45 Keywords: metal-organic frameworks, hydrogen storage, synthesizability, high-  \n46 throughput screening 47  \n48 1. Introduction  \n49 Hydrogen energy holds immense poten","cbCaivTjzXGjoKxG","https://ap.wps.com/l/cbCaivTjzXGjoKxG","pdf",2466311,1,33,"English","en",105,"# Abstract\n# Introduction\n## Hydrogen energy storage challenges\n## MOFs for hydrogen storage\n## MOF databases and high-throughput screening","[{\"question\":\"What problem does the study address in hydrogen-storage materials development?\",\"answer\":\"It targets the challenge of experimentally realizing new materials that are predicted by theory or screening, despite the speed advantages of big-data and high-throughput approaches.\"},{\"question\":\"How does the proposed method use machine learning?\",\"answer\":\"The method leverages ML techniques with MOF databases to predict the synthesizability of candidate MOF structures, guiding which porous materials are likely feasible to make.\"},{\"question\":\"What experimental result supports the proposed strategy?\",\"answer\":\"A new vanadium-based MOF was successfully synthesized and demonstrated excellent performance for cryogenic H2 storage, with reported gravimetric and volumetric uptakes at 77 K.\"}]","Machine learning-assisted design of metal–organic frameworks for hydrogen storage - A high-throughput screening and experimental approach - read and study | PDF",1786002801,83,{"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},"machine-learning-assisted-design-of-metalorganic-frameworks-for-hydrogen-storage-a-high-throughput-screening-and-experimental-approach-read-and-study","",{"@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/machine-learning-assisted-design-of-metalorganic-frameworks-for-hydrogen-storage-a-high-throughput-screening-and-experimental-approach-read-and-study/128711/",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","2026-08-23","2026-08-06",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},"What problem does the study address in hydrogen-storage materials development?","Question",{"text":76,"@type":77},"It targets the challenge of experimentally realizing new materials that are predicted by theory or screening, despite the speed advantages of big-data and high-throughput approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method use machine learning?",{"text":81,"@type":77},"The method leverages ML techniques with MOF databases to predict the synthesizability of candidate MOF structures, guiding which porous materials are likely feasible to make.",{"name":83,"@type":74,"acceptedAnswer":84},"What experimental result supports the proposed strategy?",{"text":85,"@type":77},"A new vanadium-based MOF was successfully synthesized and demonstrated excellent performance for cryogenic H2 storage, with reported gravimetric and volumetric uptakes at 77 K.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]