{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T11:49:48Z","timestamp":1744890588076},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Chemical Engineering"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1016\/j.compchemeng.2022.107739","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T01:17:45Z","timestamp":1645147065000},"page":"107739","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling"],"prefix":"10.1016","volume":"160","author":[{"given":"Zihao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yageng","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1941-5348","authenticated-orcid":false,"given":"Teng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Sundmacher","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.compchemeng.2022.107739_bib0058","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1039\/C7EE02477K","article-title":"Balancing gravimetric and volumetric hydrogen density in MOFs","volume":"10","author":"Ahmed","year":"2017","journal-title":"Energy Environ. Sci."},{"issue":"41","key":"10.1016\/j.compchemeng.2022.107739_bib0007","doi-asserted-by":"crossref","first-page":"22577","DOI":"10.1021\/acs.jpcc.0c07062","article-title":"Computational selection of high-performing covalent organic frameworks for adsorption and membrane-based CO2\/H2 separation","volume":"124","author":"Aksu","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"10.1016\/j.compchemeng.2022.107739_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.107005","article-title":"Deep learning and knowledge-based methods for computer-aided molecular design-toward a unified approach: state-of-the-art and future directions","volume":"141","author":"Alshehri","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0009","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ces.2015.09.019","article-title":"Computational screening of MOFs for C2H6\/C2H4 and C2H6\/CH4 separations","volume":"139","author":"Altintas","year":"2016","journal-title":"Chem. Eng. Sci."},{"issue":"5","key":"10.1016\/j.compchemeng.2022.107739_bib0031","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."},{"issue":"18","key":"10.1016\/j.compchemeng.2022.107739_bib0027","doi-asserted-by":"crossref","first-page":"6325","DOI":"10.1021\/acs.chemmater.8b02257","article-title":"Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning","volume":"30","author":"Anderson","year":"2018","journal-title":"Chem. Mater."},{"issue":"1","key":"10.1016\/j.compchemeng.2022.107739_bib0028","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acs.jpcc.8b09420","article-title":"Attainable volumetric targets for adsorption-based hydrogen storage in porous crystals: molecular simulation and machine learning","volume":"123","author":"Anderson","year":"2018","journal-title":"J. Phys. Chem. C"},{"issue":"37","key":"10.1016\/j.compchemeng.2022.107739_bib0008","doi-asserted-by":"crossref","first-page":"41567","DOI":"10.1021\/acsami.0c12330","article-title":"Do new MOFs perform better for CO2 capture and H2 purification? Computational screening of the updated MOF database","volume":"12","author":"Avci","year":"2020","journal-title":"ACS Appl. Mater. Interfaces"},{"issue":"49","key":"10.1016\/j.compchemeng.2022.107739_bib0004","doi-asserted-by":"crossref","first-page":"16252","DOI":"10.1002\/ange.201808716","article-title":"Molecular sieving of ethane from ethylene through the molecular cross-section size differentiation in gallate-based metal-organic frameworks","volume":"130","author":"Bao","year":"2018","journal-title":"Angew. Chem., Int. Ed."},{"issue":"48","key":"10.1016\/j.compchemeng.2022.107739_bib0057","doi-asserted-by":"crossref","first-page":"27328","DOI":"10.1021\/acs.jpcc.6b08729","article-title":"High-throughput screening of metal-organic frameworks for hydrogen storage at cryogenic temperature","volume":"120","author":"Bobbitt","year":"2016","journal-title":"J. Phys. Chem. C"},{"issue":"2","key":"10.1016\/j.compchemeng.2022.107739_bib0012","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1039\/C6ME00043F","article-title":"High-throughput computational screening of nanoporous adsorbents for CO2 capture from natural gas","volume":"1","author":"Braun","year":"2016","journal-title":"Mol. Syst. Des. Eng."},{"issue":"11","key":"10.1016\/j.compchemeng.2022.107739_bib0038","doi-asserted-by":"crossref","first-page":"6682","DOI":"10.1021\/acs.cgd.9b01050","article-title":"Identification schemes for metal-organic frameworks to enable rapid search and cheminformatics analysis","volume":"19","author":"Bucior","year":"2019","journal-title":"Cryst. Growth Des."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0003","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.ces.2017.09.032","article-title":"An ethane-trapping MOF PCN-250 for highly selective adsorption of ethane over ethylene","volume":"175","author":"Chen","year":"2018","journal-title":"Chem. Eng. Sci."},{"issue":"50","key":"10.1016\/j.compchemeng.2022.107739_bib0021","doi-asserted-by":"crossref","first-page":"27580","DOI":"10.1021\/acs.jpcc.0c09073","article-title":"Machine learning-aided computational study of metal-organic frameworks for sour gas sweetening","volume":"124","author":"Cho","year":"2020","journal-title":"J. Phys. Chem. C"},{"key":"10.1016\/j.compchemeng.2022.107739_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.ccr.2020.213487","article-title":"Applications of machine learning in metal-organic frameworks","volume":"423","author":"Chong","year":"2020","journal-title":"Coord. Chem. Rev."},{"issue":"10","key":"10.1016\/j.compchemeng.2022.107739_bib0036","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.1600909","article-title":"In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm","volume":"2","author":"Chung","year":"2016","journal-title":"Sci. Adv."},{"issue":"6295","key":"10.1016\/j.compchemeng.2022.107739_bib0005","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1126\/science.aaf2458","article-title":"Pore chemistry and size control in hybrid porous materials for acetylene capture from ethylene","volume":"353","author":"Cui","year":"2016","journal-title":"Science"},{"key":"10.1016\/j.compchemeng.2022.107739_bib0019","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/B978-0-444-63965-3.50168-9","article-title":"Comparison of tree based ensemble machine learning methods for prediction of rate constant of Diels-Alder reaction","volume":"40","author":"Dev","year":"2017","journal-title":"In Comput.-Aided Chem. Eng."},{"issue":"2","key":"10.1016\/j.compchemeng.2022.107739_bib0039","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/08927022.2015.1010082","article-title":"RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials","volume":"42","author":"Dubbeldam","year":"2016","journal-title":"Mol. Simul."},{"issue":"1\u20133","key":"10.1016\/j.compchemeng.2022.107739_bib0044","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1080\/08927022.2013.842994","article-title":"An online parameter and property database for the TraPPE force field","volume":"40","author":"Eggimann","year":"2014","journal-title":"Mol. Simul."},{"issue":"8","key":"10.1016\/j.compchemeng.2022.107739_bib0023","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1021\/jacs.9b11084","article-title":"A universal machine learning algorithm for large-scale screening of materials","volume":"142","author":"Fanourgakis","year":"2020","journal-title":"J. Am. Chem. Soc."},{"issue":"15","key":"10.1016\/j.compchemeng.2022.107739_bib0025","doi-asserted-by":"crossref","first-page":"7681","DOI":"10.1021\/jp4006422","article-title":"Large-scale quantitative structure\u2013property relationship (QSPR) analysis of methane storage in metal-organic frameworks","volume":"117","author":"Fernandez","year":"2013","journal-title":"J. Phys. Chem. C"},{"issue":"14","key":"10.1016\/j.compchemeng.2022.107739_bib0002","doi-asserted-by":"crossref","first-page":"5271","DOI":"10.1021\/ja501606h","article-title":"High methane storage capacity in aluminum metal-organic frameworks","volume":"136","author":"G\u00e1ndara","year":"2014","journal-title":"J. Am. Chem. Soc."},{"issue":"10","key":"10.1016\/j.compchemeng.2022.107739_bib0055","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1039\/C6EE02104B","article-title":"Evaluating topologically diverse metal-organic frameworks for cryo-adsorbed hydrogen storage","volume":"9","author":"G\u00f3mez-Gualdr\u00f3n","year":"2016","journal-title":"Energy Environ. Sci."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.107105","article-title":"Isotherm parameter library and evaluation software for CO2 capture adsorbents","volume":"143","author":"Ga","year":"2020","journal-title":"Comput. Chem. Eng."},{"issue":"50","key":"10.1016\/j.compchemeng.2022.107739_bib0049","doi-asserted-by":"crossref","first-page":"17704","DOI":"10.1021\/ja1089765","article-title":"Ethane\/ethene separation turned on its head: selective ethane adsorption on the metal-organic framework ZIF-7 through a gate-opening mechanism","volume":"132","author":"Gucuyener","year":"2010","journal-title":"J. Am. Chem. Soc."},{"issue":"10","key":"10.1016\/j.compchemeng.2022.107739_bib0047","doi-asserted-by":"crossref","first-page":"9107","DOI":"10.1039\/c2ee22858k","article-title":"Metal-organic frameworks with potential for energy-efficient adsorptive separation of light hydrocarbons","volume":"5","author":"He","year":"2012","journal-title":"Energy Environ. Sci."},{"issue":"16","key":"10.1016\/j.compchemeng.2022.107739_bib0034","doi-asserted-by":"crossref","first-page":"8066","DOI":"10.1021\/acs.chemrev.0c00004","article-title":"Big-data science in porous materials: materials genomics and machine learning","volume":"120","author":"Jablonka","year":"2020","journal-title":"Chem. Rev."},{"issue":"46","key":"10.1016\/j.compchemeng.2022.107739_bib0001","doi-asserted-by":"crossref","first-page":"14176","DOI":"10.1021\/ja076877g","article-title":"Impact of preparation and handling on the hydrogen storage properties of Zn4O(1, 4-benzenedicarboxylate)3 (MOF-5)","volume":"129","author":"Kaye","year":"2007","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"10.1016\/j.compchemeng.2022.107739_bib0051","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms9697","article-title":"Efficient purification of ethene by an ethane-trapping metal-organic framework","volume":"6","author":"Liao","year":"2015","journal-title":"Nat. Commun."},{"issue":"67","key":"10.1016\/j.compchemeng.2022.107739_bib0048","doi-asserted-by":"crossref","first-page":"9376","DOI":"10.1039\/C7CC04160H","article-title":"Tuning ethylene gas adsorption via metal node modulation: Cu-MOF-74 for a high ethylene deliverable capacity","volume":"53","author":"Liao","year":"2017","journal-title":"Chem. Commun."},{"issue":"14","key":"10.1016\/j.compchemeng.2022.107739_bib0045","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1021\/jp972543+","article-title":"Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes","volume":"102","author":"Martin","year":"1998","journal-title":"J. Phys. Chem. B"},{"issue":"26","key":"10.1016\/j.compchemeng.2022.107739_bib0041","doi-asserted-by":"crossref","first-page":"8897","DOI":"10.1021\/j100389a010","article-title":"DREIDING: a generic force field for molecular simulations","volume":"94","author":"Mayo","year":"1990","journal-title":"J. Phys. Chem."},{"issue":"1","key":"10.1016\/j.compchemeng.2022.107739_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-03892-8","article-title":"Computer-aided discovery of a metal-organic framework with superior oxygen uptake","volume":"9","author":"Moghadam","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.107130","article-title":"A comprehensive methodology to screen metal-organic frameworks towards sustainable photofixation of nitrogen","volume":"144","author":"Mohamed","year":"2021","journal-title":"Comput. Chem. Eng."},{"issue":"39","key":"10.1016\/j.compchemeng.2022.107739_bib0050","doi-asserted-by":"crossref","first-page":"5882","DOI":"10.1039\/C7CE01438D","article-title":"Synthesis of a partially fluorinated ZIF-8 analog for ethane\/ethene separation","volume":"19","author":"Mondal","year":"2017","journal-title":"CrystEngComm"},{"issue":"1","key":"10.1016\/j.compchemeng.2022.107739_bib0032","doi-asserted-by":"crossref","first-page":"1","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."},{"issue":"10","key":"10.1016\/j.compchemeng.2022.107739_bib0026","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1021\/acscombsci.7b00056","article-title":"Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs)","volume":"19","author":"Pardakhti","year":"2017","journal-title":"ACS Comb. Sci."},{"issue":"41","key":"10.1016\/j.compchemeng.2022.107739_bib0010","doi-asserted-by":"crossref","first-page":"15904","DOI":"10.1039\/C6TA06262H","article-title":"High-throughput computational screening of 137953 metal-organic frameworks for membrane separation of a CO2\/N2\/CH4 mixture","volume":"4","author":"Qiao","year":"2016","journal-title":"J. Mater. Chem. A"},{"issue":"10","key":"10.1016\/j.compchemeng.2022.107739_bib0033","doi-asserted-by":"crossref","first-page":"e17352","DOI":"10.1002\/aic.17352","article-title":"Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal-organic frameworks","volume":"67","author":"Qiao","year":"2021","journal-title":"AIChE J"},{"issue":"8","key":"10.1016\/j.compchemeng.2022.107739_bib0017","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1039\/D0ME00067A","article-title":"Design of fragrant molecules through the incorporation of rough sets into computer-aided molecular design","volume":"5","author":"Radhakrishnapany","year":"2020","journal-title":"Mol. Syst. Des. Eng."},{"issue":"25","key":"10.1016\/j.compchemeng.2022.107739_bib0042","doi-asserted-by":"crossref","first-page":"10024","DOI":"10.1021\/ja00051a040","article-title":"UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations","volume":"114","author":"Rapp\u00e9","year":"1992","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2019.115430","article-title":"Machine learning and in silico discovery of metal-organic frameworks: methanol as a working fluid in adsorption-driven heat pumps and chillers","volume":"214","author":"Shi","year":"2020","journal-title":"Chem. Eng. Sci."},{"issue":"4","key":"10.1016\/j.compchemeng.2022.107739_bib0024","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1039\/C4EE03515A","article-title":"The materials genome in action: identifying the performance limits for methane storage","volume":"8","author":"Simon","year":"2015","journal-title":"Energy Environ. Sci."},{"issue":"3","key":"10.1016\/j.compchemeng.2022.107739_bib0040","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1021\/i160019a011","article-title":"Molecular parameters for normal fluids. Lennard-Jones 12-6 Potential","volume":"5","author":"Tee","year":"1966","journal-title":"Ind. Eng. Chem. Fundam."},{"issue":"33","key":"10.1016\/j.compchemeng.2022.107739_bib0046","doi-asserted-by":"crossref","first-page":"8008","DOI":"10.1021\/jp001044x","article-title":"Transferable potentials for phase equilibria. 4. United-atom description of linear and branched alkenes and alkylbenzenes","volume":"104","author":"Wick","year":"2000","journal-title":"J. Phys. Chem. B"},{"issue":"1","key":"10.1016\/j.compchemeng.2022.107739_bib0052","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.micromeso.2011.08.020","article-title":"Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials","volume":"149","author":"Willems","year":"2012","journal-title":"Microporous Mesoporous Mater"},{"issue":"2","key":"10.1016\/j.compchemeng.2022.107739_bib0037","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."},{"issue":"17","key":"10.1016\/j.compchemeng.2022.107739_bib0043","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1021\/jz3008485","article-title":"An extended charge equilibration method","volume":"3","author":"Wilmer","year":"2012","journal-title":"J. Phys. Chem. Lett."},{"issue":"9","key":"10.1016\/j.compchemeng.2022.107739_bib0006","doi-asserted-by":"crossref","first-page":"6263","DOI":"10.1039\/C6SC01477A","article-title":"In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis","volume":"7","author":"Witman","year":"2016","journal-title":"Chem. Sci."},{"issue":"14","key":"10.1016\/j.compchemeng.2022.107739_bib0022","doi-asserted-by":"crossref","first-page":"8550","DOI":"10.1021\/acs.jpcc.8b11793","article-title":"Understanding quantitative relationship between methane storage capacities and characteristic properties of metal-organic frameworks based on machine learning","volume":"123","author":"Wu","year":"2019","journal-title":"J. Phys. Chem. C"},{"key":"10.1016\/j.compchemeng.2022.107739_bib0016","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.compchemeng.2018.04.018","article-title":"A machine learning based computer-aided molecular design\/screening methodology for fragrance molecules","volume":"115","author":"Zhang","year":"2018","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2022.107739_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2021.116947","article-title":"Odor prediction and aroma mixture design using machine learning model and molecular surface charge density profiles","volume":"245","author":"Zhang","year":"2021","journal-title":"Chem. Eng. Sci."},{"issue":"6","key":"10.1016\/j.compchemeng.2022.107739_bib0020","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.eng.2019.02.011","article-title":"Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design","volume":"5","author":"Zhou","year":"2019","journal-title":"Engineering"},{"key":"10.1016\/j.compchemeng.2022.107739_bib0011","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/B978-0-12-823377-1.50150-6","article-title":"In silico screening of metal-organic frameworks for acetylene\/ethylene separation","volume":"48","author":"Zhou","year":"2020","journal-title":"In Comput.-Aided Chem. Eng."}],"container-title":["Computers & Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135422000801?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135422000801?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T06:39:21Z","timestamp":1672814361000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135422000801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":55,"alternative-id":["S0098135422000801"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2022.107739","relation":{},"ISSN":["0098-1354"],"issn-type":[{"type":"print","value":"0098-1354"}],"subject":[],"published":{"date-parts":[[2022,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2022.107739","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107739"}}