[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125706-105":59,"doc-detail-125706-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","a-social-network-guided-approach-to-machine-learning-for-metal-organic-framework-property-prediction-read-online","A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction - read online","","Reducing experimental and computational cost is central to accurate materials property prediction for metal-organic frameworks (MOFs) when datasets grow in size. The study introduces MOFGalaxyNet, transforming MOFs into a social-network representation built from geometric descriptors from a MOF database. Social network analytics and graph-mining extract community structures whose neighboring-MOF characteristics support property estimation. Evaluations for CO2 and CH4 gas adsorption show improved prediction accuracy versus alternative machine learning algorithms. The method enables high-throughput screening, anomaly detection, scalable expansion, and transfer to other materials such as high-entropy alloys.",{"@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/a-social-network-guided-approach-to-machine-learning-for-metal-organic-framework-property-prediction-read-online/125706/",{"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/a-social-network-guided-approach-to-machine-learning-for-metal-organic-framework-property-prediction-read-online/125706.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does MOFGalaxyNet address in MOF property prediction?","Question",{"text":113,"@type":114},"It targets the need to reduce experimental and computational time while improving the quality of materials property predictions as MOF research datasets become abundant.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is MOFGalaxyNet constructed?",{"text":118,"@type":114},"The framework converts MOFs into a social network (MOFGalaxyNet) using geometric descriptors derived from the MOF database, then applies social network analytics and graph-mining.",{"name":120,"@type":111,"acceptedAnswer":121},"How does MOFGalaxyNet perform for predicting gas adsorption of CO2 and CH4?",{"text":122,"@type":114},"Communities extracted from MOFGalaxyNet are used to evaluate prediction performance for CO2 and CH4, and results show higher accuracy than other tested machine learning algorithms based on mean absolute error (MAE).","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},125706,1785900755,{"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":8,"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":81},962084925636,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","A Social Network-Guided Approach to Machine Learning for Metal  \nOrganic Framework Property Prediction  \nMehrdad Jalali, A.D. Dinga Wonanke, and Christof Wöll  \nInstitute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany  \nMotivation  \n􀂃 Reducing experimental and computational time and improving the quality of materials properties prediction is crucial when dealing with abundant research data in metal-organic framework (MOF) materials.  \n􀂃 Graph learning and social network analysis (SNA), an ML-based method, can be employed to analyze the materials data within a social network framework to address these challenges.  \n􀂃 This research aims to convert MOFs into a social network, referred to as MOFGalaxyNet, and then utilize this social network to enhance the accuracy of gas adsorption prediction. This network draws inspiration from the phenomenon of galaxies.  \nMethod  \n􀂃 This study created a social network, MOFGalaxyNet, using geometric descriptors obtained from the MOFs database.  \n􀂃 Following the development of MOFGalaxyNet, social network analytics were used in conjunction with graph-mining algorithms to extract valuable insights from the MOF data.  \nMOFs Dataset  \nMOFGalaxyNet  \nExperimental Results  \nMOFGalaxyNet Communities  \n􀂃 Community detection refers to identifying MOF groups with similar structural and chemical characteristics.  \n􀂃 Considering the characteristics of neighboring MOFs in the community can predict the properties of a specific MOF.  \n􀂃 Community detection provides a high-throughput screening method for MOFs datasets and can detect anomalies in materials data.  \nMOFGalaxyNet to Predict Gas Adsorption  \n􀂃 An evaluation was conducted to determine the effectiveness of using communities extracted from MOFGalaxyNet to predict MOFs' gas adsorption properties for CO2 and CH4 .  \n􀂃 The study evaluated the effectiveness of various machine learning algorithms and compared them to MOFGalaxyNet using the mean absolute error (MAE) . The results indicated that MOFGalaxyNet was more accurate than the other algorithms.  \nConclusion and Future Direction  \nRevolutionizing Material Discovery: The Superiority of Social Network Analysis Over Conventional Machine Learning  \nMOFGalaxyNet  \n􀂃 The suggested SNA methodology can expedite the analysis of MOF structures, even with increased theoretical and experimental data.  \n􀂃 The innovative MOFGalaxyNet framework can be expanded to manage vast MOF databases.  \n􀂃 This approach can be extended to other materials, such as high-entropy alloys (HEAs) .  \nReferences and Related Published Papers  \nI. Jalali, M. , Tsotsalas , M. , & Wöll, C. (2022) . MOFGalaxyNet: Exploiting MetalOrganic Framework Relationships via Social Network Analysis. Nanomaterials.  \nII. Ghouchan Nezhad Noor Nia, R. , Jalali, M. , Mail, M. , Ivanisenko , Y. , & Kübel, C.(2022) . Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network. ACS omega.  \nKIT – The Research University in [the Helmholtz Association](the Helmholtz Association www.kit.edu)[ www.kit.edu](the Helmholtz Association www.kit.edu)","cbCaik4yHtWaSVyK","https://ap.wps.com/l/cbCaik4yHtWaSVyK","pdf",2017271,"English","# Motivation\n# Method\n# MOFs Dataset\n# MOFGalaxyNet Communities\n## Community detection for similar MOFs\n## High-throughput screening and anomaly detection\n# MOFGalaxyNet to Predict Gas Adsorption\n## CO2 and CH4 evaluation\n## Comparison with other ML algorithms\n# Conclusion and Future Direction\n# References and Related Published Papers","[{\"question\":\"What problem does MOFGalaxyNet address in MOF property prediction?\",\"answer\":\"It targets the need to reduce experimental and computational time while improving the quality of materials property predictions as MOF research datasets become abundant.\"},{\"question\":\"How is MOFGalaxyNet constructed?\",\"answer\":\"The framework converts MOFs into a social network (MOFGalaxyNet) using geometric descriptors derived from the MOF database, then applies social network analytics and graph-mining.\"},{\"question\":\"How does MOFGalaxyNet perform for predicting gas adsorption of CO2 and CH4?\",\"answer\":\"Communities extracted from MOFGalaxyNet are used to evaluate prediction performance for CO2 and CH4, and results show higher accuracy than other tested machine learning algorithms based on mean absolute error (MAE).\"}]","A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction - read online | PDF"]