[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124911-en":3,"doc-seo-124911-105":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},124911,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Leveraging machine learning in porous media","Artificial intelligence and especially machine learning (ML) have significantly transformed engineering and fundamental sciences by improving data processing and enabling new technologies. ML is expected to enhance understanding and prediction of porous-media behavior through learning from large subsurface and experimental datasets, supporting accurate simulations and aiding optimization of porous-material design. This open-access review summarizes the current intersection of ML and porous media across six application areas, highlighting progress while detailing challenges and future opportunities, and listing supplementary literature databases and online datasets. Future directions emphasize hybrid ML–physics-based models for better accuracy and interpretability.","Open Access Article . Pu on 19 Ju 2024. Down on 8/5/2024blished ly loaded 3:48: 13 PM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution npor e nce.  \nJournal of  \nMaterials Chemistry A  \nREVIEW  \nView Article Online View Journal  \nCite this: DOI: 10 .1039/d4ta00251b  \nReceived 25th January 2024 Accepted 24th May 2024  \nDOI: 10.1039/d4ta00251b[rsc.li/materials-a](rsc.li/materials-a)  \nLeveraging machine learning in porous media†  \nMostafa Delpisheh,  *a Benyamin Ebrahimpour,  b Abolfazl Fattahi,  c Majid Siavashi, d Hamed Mir, d Hossein Mashhadimoslem,  e  \nMohammad Ali Abdol,  e Mina Ghorbani, f Javad Shokri,  g Daniel Niblett,  a Khabat Khosravi,  h Shayan Rahimi,  i Seyed Mojtaba Alirahmi, j Haoshui Yu, j Ali Elkamel, ke Vahid Niasar  *g and Mohamed Mamlouk  *a  \nThe emergence of artificial intelligence (AI) and, more particularly, machine learning (ML), has hada significant impact on engineering and the fundamental sciences, resulting in advances in various fields. The use of ML has significantly enhanced data processing and analysis, eliciting the development of new and improved technologies. Specifically, ML is projected to play an increasingly significant role in helping researchers better understand and predict the behavior of porous media. Furthermore, ML models will be able to make use of sizable datasets, such as subsurface data and experiments, to produce accurate predictions and simulations of porous media systems. This capability could help optimize the design of porous materials for specific applications and improve the effectiveness of industrial processes. To this end, this review paper attempts to provide an overview of the present status quo in this context, i.e., the interface of ML and porous media in six different applications, namely, heat exchanger and storage, energy storage and combustion, electrochemical devices, hydrocarbon reservoirs, carbon capture and sequestration, and groundwater, stressing the advances made in the application of ML to porous media and offering insights into the challenges and opportunities for future research. Each section also entails a supplementary database of the literature as a spreadsheet, which includes the details of ML models, datasets, key findings, etc., and mentions relevant available online datasets that can be used to train ML models. Future research trends include employing hybrid models by combining ML models with physicsbased models of porous media to improve predictions concerning accuracy and interpretability.  \n1. Introduction  \naSchool of Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK.  \n[E-mail: m.delpisheh2@ncl.ac.uk](E-mail: m.delpisheh2@ncl.ac.uk); [mohamed.mamlouk@ncl.ac.uk](mohamed.mamlouk@ncl.ac.uk)  \nbSchool of Mathematics and Physics, University of Portsmouth, Portsmouth, UK cDepartment of Mechanical Engineering, University of Kashan, Kashan, Iran dSchool of Mechanical Engineering, Iran University of Science and Technology, Iran eDepartment of Chemical Engineering, University of Waterloo, Waterloo, N2L3G1, Canada  \nfSchool of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran  \ngDepartment of Chemical Engineering, University of Manchester, Oxford Road,  \nManchester, M13 9PL, UK. E-mail: [vahid.niasar@manchester.ac.uk](vahid.niasar@manchester.ac.uk)  \nhSchool of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PEI, Canada  \niDepartment of Chemical Engineering & Materials Science, University of Southern California, USA  \njDepartment of Chemistry and Bioscience, Aalborg University, Niels Bohrs Vej 8A, Esbjerg 6700, Denmark  \nkDepartment of Chemical and Petroleum Engineering, Khalifa University, Abu Dhabi, UAE  \n† Electronic supplementary information (ESI) available. See DOI:  \n[https://doi.org/10.1039/d4ta00251b](https://doi.org/10.1039/d4ta00251b)  \nPorous media play an important role in many natural and engineered systems, including s","cbCaipG64PRgkv5k","https://ap.wps.com/l/cbCaipG64PRgkv5k","pdf",20175494,1,66,"English","en",105,"# Introduction\n# Porous media applications of ML\n## Heat exchanger and storage\n## Energy storage and combustion\n## Electrochemical devices\n## Hydrocarbon reservoirs\n## Carbon capture and sequestration\n## Groundwater\n# Future research directions","[{\"question\":\"为什么需要在多孔介质研究中引入机器学习（ML）？\",\"answer\":\"ML能更好地处理与分析数据，并学习来自地下与实验的较大数据集，从而提升对多孔介质行为的理解与预测能力。\"},{\"question\":\"这份综述覆盖了哪些多孔介质应用方向？\",\"answer\":\"综述从ML与多孔介质的接口出发，重点讨论热交换器与储能、能量储存与燃烧、电化学器件、烃类油藏、碳捕获与封存以及地下水等六类应用。\"},{\"question\":\"未来研究有哪些主要趋势？\",\"answer\":\"未来趋势包括将ML与多孔介质的物理模型进行混合，提升预测的准确性与可解释性，同时结合可用的数据集与文献数据库支持模型训练。\"}]","Leveraging machine learning in porous media | PDF",1785895353,166,{"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},"leveraging-machine-learning-in-porous-media","",{"@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/leveraging-machine-learning-in-porous-media/124911/",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","2026-08-05",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},"为什么需要在多孔介质研究中引入机器学习（ML）？","Question",{"text":75,"@type":76},"ML能更好地处理与分析数据，并学习来自地下与实验的较大数据集，从而提升对多孔介质行为的理解与预测能力。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"这份综述覆盖了哪些多孔介质应用方向？",{"text":80,"@type":76},"综述从ML与多孔介质的接口出发，重点讨论热交换器与储能、能量储存与燃烧、电化学器件、烃类油藏、碳捕获与封存以及地下水等六类应用。",{"name":82,"@type":73,"acceptedAnswer":83},"未来研究有哪些主要趋势？",{"text":84,"@type":76},"未来趋势包括将ML与多孔介质的物理模型进行混合，提升预测的准确性与可解释性，同时结合可用的数据集与文献数据库支持模型训练。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]