{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:41:58Z","timestamp":1785404518667,"version":"3.56.0"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian Province Young and Middle-aged Scientists Fund","award":["JAT241138"],"award-info":[{"award-number":["JAT241138"]}]},{"name":"Fujian Province Young and Middle-aged Scientists Fund","award":["JAT241139"],"award-info":[{"award-number":["JAT241139"]}]},{"name":"Fujian Province Youth Science Fund","award":["2025J08093"],"award-info":[{"award-number":["2025J08093"]}]},{"name":"Fujian Province Natural Science Fund","award":["2025J011710"],"award-info":[{"award-number":["2025J011710"]}]},{"DOI":"10.13039\/501100008961","name":"Longyan University","doi-asserted-by":"crossref","award":["LB2025003"],"award-info":[{"award-number":["LB2025003"]}],"id":[{"id":"10.13039\/501100008961","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100008961","name":"Longyan University","doi-asserted-by":"crossref","award":["LB2025009"],"award-info":[{"award-number":["LB2025009"]}],"id":[{"id":"10.13039\/501100008961","id-type":"DOI","asserted-by":"crossref"}]},{"award":["LB2025003"],"award-info":[{"award-number":["LB2025003"]}],"id":[{"id":"https:\/\/ror.org\/0483s5p06","id-type":"ROR","asserted-by":"publisher"}]},{"award":["LB2025009"],"award-info":[{"award-number":["LB2025009"]}],"id":[{"id":"https:\/\/ror.org\/0483s5p06","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nanomaterials"],"abstract":"<jats:p>Machine learning has created new opportunities for gas adsorption research using nanoporous materials, but the field\u2019s evolution remains insufficiently quantified. This study retrieved literature from the Web of Science Core Collection for 2010\u20132026 and retained 730 valid records from 1581 initial publications after screening. VOSviewer, CiteSpace, and R were used to analyze publication growth, collaboration networks, journal sources, and thematic evolution. Results show that annual output remained generally below 20 before 2019, then increased rapidly and reached approximately 280 publications in 2025, indicating accelerated integration of machine learning with adsorption simulation, material screening, and performance evaluation. The source distribution broadened from a limited set of chemistry and engineering journals to diverse venues, with recent high publication weights in Chemical Engineering Journal, Separation and Purification Technology, ACS Applied Materials &amp; Interfaces, Microporous and Mesoporous Materials, and Journal of Materials Chemistry A. Collaboration analysis identified 10 compact author clusters, including groups associated with Randall Q. Snurr, Seda Keskin, Zhiwei Qiao, Qingyuan Yang, and Chongli Zhong, whereas the weak bridging links among clusters indicate that cross-community collaboration remains limited. Country and institutional analyses show that China, the United States, Canada, Iran, India, South Korea, and the United Kingdom are leading contributors, with Guangzhou University, Ko\u00e7 University, Northwestern University, the Chinese Academy of Sciences, Beijing University of Chemical Technology, and the United States Department of Energy occupying prominent positions. Keyword evolution reveals a shift from adsorption behavior and porous adsorbents toward data-guided material selection, high-throughput screening, deep learning, Bayesian optimization, and performance optimization, offering guidance for data-driven adsorbent discovery.<\/jats:p>","DOI":"10.3390\/nano16140883","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:00:17Z","timestamp":1784548817000},"page":"883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials"],"prefix":"10.3390","volume":"16","author":[{"given":"Xin","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Physics and Mechanical and Electronical Engineering, Longyan University, Longyan 364012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiong","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Physics and Mechanical and Electronical Engineering, Longyan University, Longyan 364012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixia","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Longyan University, Longyan 364000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128238","DOI":"10.1016\/j.seppur.2024.128238","article-title":"Advances in porous materials for efficient separation and purification of flue gas","volume":"352","author":"Wei","year":"2025","journal-title":"Sep. Purif. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24310","DOI":"10.1021\/acsami.4c22163","article-title":"Preparation of hierarchical porous zeolite particles with multiscale pore architectures through a template-assisted spray process for enhanced toluene adsorption rate","volume":"17","author":"Cao","year":"2025","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20915","DOI":"10.1002\/anie.202107156","article-title":"Porous metal-organic framework liquids for enhanced CO2 adsorption and catalytic conversion","volume":"60","author":"Zou","year":"2021","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1021\/cr2003272","article-title":"Carbon dioxide capture in metal\u2013organic frameworks","volume":"112","author":"Sumida","year":"2012","journal-title":"Chem. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17647","DOI":"10.1021\/acs.chemrev.2c00140","article-title":"Zeolites in adsorption processes: State of the art and future prospects","volume":"122","author":"Valencia","year":"2022","journal-title":"Chem. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1039\/C3SC52633J","article-title":"Evaluating metal-organic frameworks for natural gas storage","volume":"5","author":"Mason","year":"2014","journal-title":"Chem. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11586","DOI":"10.1002\/anie.201101891","article-title":"Development and evaluation of porous materials for carbon dioxide separation and capture","volume":"50","author":"Bae","year":"2011","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1039\/b802256a","article-title":"Hydrogen storage in metal-organic frameworks","volume":"38","author":"Murray","year":"2009","journal-title":"Chem. Soc. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1126\/science.1192160","article-title":"Ultrahigh porosity in metal-organic frameworks","volume":"329","author":"Furukawa","year":"2010","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1126\/science.1120411","article-title":"Porous, crystalline, covalent organic frameworks","volume":"310","author":"Benin","year":"2005","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"eaal1585","DOI":"10.1126\/science.aal1585","article-title":"The atom, the molecule, and the covalent organic framework","volume":"355","author":"Diercks","year":"2017","journal-title":"Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9457","DOI":"10.1002\/anie.200904637","article-title":"Targeted synthesis of a porous aromatic framework with high stability and exceptionally high surface area","volume":"48","author":"Ben","year":"2009","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1038\/nchem.1192","article-title":"Large-scale screening of hypothetical metal-organic frameworks","volume":"4","author":"Wilmer","year":"2012","journal-title":"Nat. Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6185","DOI":"10.1021\/cm502594j","article-title":"Computation-ready, experimental metal-organic frameworks: A tool to enable high-throughput screening of nanoporous crystals","volume":"26","author":"Chung","year":"2014","journal-title":"Chem. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5985","DOI":"10.1021\/acs.jced.9b00835","article-title":"Advances, updates, and analytics for the computation-ready, experimental metal-organic framework database: CoRE MOF 2019","volume":"64","author":"Chung","year":"2019","journal-title":"J. Chem. Eng. Data"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/s41586-019-1798-7","article-title":"Data-driven design of metal-organic frameworks for wet flue gas CO2 capture","volume":"576","author":"Boyd","year":"2019","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1021\/acs.jced.2c00583","article-title":"MOFX-DB: An online database of computational adsorption data for nanoporous materials","volume":"68","author":"Bobbitt","year":"2023","journal-title":"J. Chem. Eng. Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1021\/jz501331m","article-title":"Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture","volume":"5","author":"Fernandez","year":"2014","journal-title":"J. Phys. Chem. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4133","DOI":"10.1021\/acs.jpcc.8b10644","article-title":"Robust machine learning models for predicting high CO2 working capacity and CO2\/H2 selectivity of gas adsorption in metal organic frameworks for precombustion carbon capture","volume":"123","author":"Dureckova","year":"2019","journal-title":"J. Phys. Chem. C"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1039\/C8ME00050F","article-title":"Energy-based descriptors to rapidly predict hydrogen storage in metal-organic frameworks","volume":"4","author":"Bucior","year":"2019","journal-title":"Mol. Syst. Des. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7117","DOI":"10.1021\/acs.jpcc.9b10766","article-title":"A generic machine learning algorithm for the prediction of gas adsorption in nanoporous materials","volume":"124","author":"Fanourgakis","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1039\/D0ME00005A","article-title":"Machine-learning-assisted high-throughput computational screening of high performance metal-organic frameworks","volume":"5","author":"Shi","year":"2020","journal-title":"Mol. Syst. Des. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1021\/acs.jcim.1c00191","article-title":"Machine learning meets with metal organic frameworks for gas storage and separation","volume":"61","author":"Altintas","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27996","DOI":"10.1021\/acs.jpcc.0c06334","article-title":"High-performing deep learning regression models for predicting low-pressure CO2 adsorption properties of metal-organic frameworks","volume":"124","author":"Burner","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1021\/acs.jctc.9b00940","article-title":"Adsorption isotherm predictions for multiple molecules in MOFs using the same deep learning model","volume":"16","author":"Anderson","year":"2020","journal-title":"J. Chem. Theory Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, W., Liu, J., Dong, F., Chen, R., Das, J., Ge, W., Xu, X., and Hong, H. (2022). Deep learning models for predicting gas adsorption capacity of nanomaterials. Nanomaterials, 12.","DOI":"10.3390\/nano12193376"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4636","DOI":"10.1021\/acs.jcim.9b00623","article-title":"Machine learning prediction on properties of nanoporous materials utilizing pore geometry barcodes","volume":"59","author":"Zhang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9360","DOI":"10.1021\/acs.jpcc.0c01167","article-title":"Topological descriptors help predict guest adsorption in nanoporous materials","volume":"124","author":"Krishnapriyan","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"145301","DOI":"10.1103\/PhysRevLett.120.145301","article-title":"Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties","volume":"120","author":"Xie","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.1021\/acs.jcim.2c00092","article-title":"Deep-learning-based end-to-end predictions of CO2 capture in metal-organic frameworks","volume":"62","author":"Lu","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4068","DOI":"10.1038\/s41467-020-17755-8","article-title":"Understanding the diversity of the metal-organic framework ecosystem","volume":"11","author":"Moosavi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"61004","DOI":"10.1021\/acsami.1c16220","article-title":"Diversifying databases of metal organic frameworks for high-throughput computational screening","volume":"13","author":"Majumdar","year":"2021","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2958","DOI":"10.1021\/jacs.2c11420","article-title":"MOFormer: Self-supervised transformer model for metal-organic framework property prediction","volume":"145","author":"Cao","year":"2023","journal-title":"J. Am. Chem. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/s42256-023-00628-2","article-title":"A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks","volume":"5","author":"Kang","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1038\/s41467-024-46276-x","article-title":"A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks","volume":"15","author":"Wang","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.1039\/D3SC05612K","article-title":"Machine learning potential for modelling H2 adsorption\/diffusion in MOFs with open metal sites","volume":"15","author":"Liu","year":"2024","journal-title":"Chem. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17535","DOI":"10.1021\/jacs.1c07217","article-title":"Using machine learning and data mining to leverage community knowledge for the engineering of stable metal-organic frameworks","volume":"143","author":"Nandy","year":"2021","journal-title":"J. Am. Chem. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"215112","DOI":"10.1016\/j.ccr.2023.215112","article-title":"Recent advances in computational modeling of MOFs: From molecular simulations to machine learning","volume":"484","author":"Demir","year":"2023","journal-title":"Coord. Chem. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4222","DOI":"10.1021\/acs.langmuir.1c00122","article-title":"A universal standard archive file for adsorption data","volume":"37","author":"Evans","year":"2021","journal-title":"Langmuir"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5785","DOI":"10.1021\/acs.jcim.1c01205","article-title":"Correlation between the metal and organic components, structure property, and gas-adsorption capacity of metal-organic frameworks","volume":"61","author":"Yuyama","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41524-017-0045-8","article-title":"Chemically intuited, large-scale screening of MOFs by machine learning techniques","volume":"3","author":"Borboudakis","year":"2017","journal-title":"npj Comput. Mater."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41560-023-01417-2","article-title":"Progress toward the computational discovery of new metal-organic framework adsorbents for energy applications","volume":"9","author":"Moghadam","year":"2024","journal-title":"Nat. Energy"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yu, S., Xu, K., Wang, Z., Zhang, Z., and Zhang, Z. (2023). Bibliometric and visualized analysis of metal-organic frameworks in biomedical application. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1190654"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nawaz, M., Suleman, H., and Maulud, A.S. (2022). Carbon capture and utilization: A bibliometric analysis from 2007\u20132021. Energies, 15.","DOI":"10.3390\/en15186611"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1007\/s11837-024-07065-5","article-title":"Artificial intelligence in metal-organic frameworks from 2013 to 2024: A bibliometric analysis","volume":"77","author":"Cao","year":"2025","journal-title":"JOM"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1002\/asi.20317","article-title":"CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature","volume":"57","author":"Chen","year":"2006","journal-title":"J. Am. Soc. Inf. Sci. Technol."}],"container-title":["Nanomaterials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-4991\/16\/14\/883\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:34:01Z","timestamp":1784608441000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-4991\/16\/14\/883"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["nano16140883"],"URL":"https:\/\/doi.org\/10.3390\/nano16140883","relation":{},"ISSN":["2079-4991"],"issn-type":[{"value":"2079-4991","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]}}}