{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T22:01:53Z","timestamp":1780178513238,"version":"3.54.0"},"reference-count":48,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["chemistry-europe.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ChemPhysChem"],"published-print":{"date-parts":[[2026,5,27]]},"abstract":"<jats:p>\n                    The adsorption of carbon tetrachloride (CTC) in MOFs was investigated by integrating GCMC simulations at 298.15\u2009K and 10\u2009kPa with machine learning (ML). The structural properties, including the largest cavity diameter, pore limiting diameter, and accessible surface area, were used as features in the ML models. Four algorithms, including Random Forest, LightGBM, XGBoost, and CatBoost, were employed to predict the CTC adsorptions. CatBoost demonstrated superior accuracy, achieving an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    of 0.859 and an RMSE of 0.049. K\u2010Means clustering identified MOF clusters with high average adsorptions and highlighted the prevalence of metals such as Zn, Co, and Cd. The key features influencing adsorption included heat of adsorption, specific accessible volume, gravimetric surface area, volumetric surface area, and specific accessible volume. Mann\u2013Whitney\n                    <jats:italic>U<\/jats:italic>\n                    analysis of the top performing MOFs revealed that Void Fraction, gravimetric surface area, and specific accessible volume are the most important features for the adsorption of CTC in MOFs.\n                  <\/jats:p>","DOI":"10.1002\/cphc.202500838","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T03:09:32Z","timestamp":1779505772000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Guided Discovery of Efficient Metal\u2013Organic Frameworks for Volatile Organic Compound Removal: A Case Study on CCl\n                    <sub>4<\/sub>"],"prefix":"10.1002","volume":"27","author":[{"given":"Mohammad Mehdi","family":"Moghtadaei","sequence":"first","affiliation":[{"name":"Department of Physical Chemistry, Faculty of Chemistry University of Mazandaran  Babolsar Mazandaran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-479X","authenticated-orcid":false,"given":"Saeid","family":"Yeganegi","sequence":"additional","affiliation":[{"name":"Department of Physical Chemistry, Faculty of Chemistry University of Mazandaran  Babolsar Mazandaran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8478-5399","authenticated-orcid":false,"given":"Mohammad Hossein","family":"Fatemi","sequence":"additional","affiliation":[{"name":"Department of Analytical Chemistry, Faculty of Chemistry University of Mazandaran  Babolsar Mazandaran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"e_1_2_10_2_1","volume-title":"Volatile Organic Compounds in the Atmosphere","author":"Carpenter P. 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