Publication:
Accelerated discovery of metal-organic frameworks for CO2 capture by artificial intelligence

dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorKeskin, Seda
dc.contributor.kuauthorAksu, Gökhan Önder
dc.contributor.kuauthorErçakır, Göktuğ
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2025-01-19T10:31:12Z
dc.date.issued2024
dc.description.abstractThe existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture. © 2023 The Authors. Published by American Chemical Society.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue1
dc.description.openaccessAll Open Access; Green Open Access; Hybrid Gold Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipS.K. acknowledges the ERC-2017-Starting Grant. This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC-2017-Starting Grant, Grant Agreement No. 756489-COSMOS). The authors thank Dr. Cigdem Altintas for fruitful discussions.
dc.description.volume63
dc.identifier.doi10.1021/acs.iecr.3c03817
dc.identifier.eissn1520-5045
dc.identifier.issn0888-5885
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85181575186
dc.identifier.urihttps://doi.org/10.1021/acs.iecr.3c03817
dc.identifier.urihttps://hdl.handle.net/20.500.14288/26183
dc.identifier.wos1140780000001
dc.keywordsArtificial intelligence
dc.keywordsCarbon dioxide
dc.keywordsClimate change
dc.keywordsMembranes
dc.keywordsMetal-Organic Frameworks
dc.keywordsBrute force
dc.keywordsExperimental testing
dc.keywordsInnovative technology
dc.keywordsMetal organic metals
dc.keywordsMetalorganic frameworks (MOFs)
dc.keywordsMolecular simulations
dc.language.isoeng
dc.publisherAmerican Chemical Society
dc.relation.grantnoERC-2017-Starting; Horizon 2020 Framework Programme, H2020, (756489-COSMOS); European Research Council, ERC
dc.relation.ispartofIndustrial and Engineering Chemistry Research
dc.subjectBiochemistry and molecular biologyy
dc.titleAccelerated discovery of metal-organic frameworks for CO2 capture by artificial intelligence
dc.typeReview
dspace.entity.typePublication
local.contributor.kuauthorGülbakan, Hasan Can
local.contributor.kuauthorKeskin, Seda
local.contributor.kuauthorAksu, Gökhan Önder
local.contributor.kuauthorErçakır, Göktuğ
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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