Publication: Accelerated discovery of metal-organic frameworks for CO2 capture by artificial intelligence
dc.contributor.department | Department of Chemical and Biological Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Keskin, Seda | |
dc.contributor.kuauthor | Aksu, Gökhan Önder | |
dc.contributor.kuauthor | Erçakır, Göktuğ | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.date.accessioned | 2025-01-19T10:31:12Z | |
dc.date.issued | 2024 | |
dc.description.abstract | The 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.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.indexedby | PubMed | |
dc.description.issue | 1 | |
dc.description.openaccess | All Open Access; Green Open Access; Hybrid Gold Open Access | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | S.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.volume | 63 | |
dc.identifier.doi | 10.1021/acs.iecr.3c03817 | |
dc.identifier.eissn | 1520-5045 | |
dc.identifier.issn | 0888-5885 | |
dc.identifier.quartile | Q2 | |
dc.identifier.scopus | 2-s2.0-85181575186 | |
dc.identifier.uri | https://doi.org/10.1021/acs.iecr.3c03817 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/26183 | |
dc.identifier.wos | 1140780000001 | |
dc.keywords | Artificial intelligence | |
dc.keywords | Carbon dioxide | |
dc.keywords | Climate change | |
dc.keywords | Membranes | |
dc.keywords | Metal-Organic Frameworks | |
dc.keywords | Brute force | |
dc.keywords | Experimental testing | |
dc.keywords | Innovative technology | |
dc.keywords | Metal organic metals | |
dc.keywords | Metalorganic frameworks (MOFs) | |
dc.keywords | Molecular simulations | |
dc.language.iso | eng | |
dc.publisher | American Chemical Society | |
dc.relation.grantno | ERC-2017-Starting; Horizon 2020 Framework Programme, H2020, (756489-COSMOS); European Research Council, ERC | |
dc.relation.ispartof | Industrial and Engineering Chemistry Research | |
dc.subject | Biochemistry and molecular biologyy | |
dc.title | Accelerated discovery of metal-organic frameworks for CO2 capture by artificial intelligence | |
dc.type | Review | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Gülbakan, Hasan Can | |
local.contributor.kuauthor | Keskin, Seda | |
local.contributor.kuauthor | Aksu, Gökhan Önder | |
local.contributor.kuauthor | Erçakır, Göktuğ | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit2 | Department of Chemical and Biological Engineering | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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relation.isOrgUnitOfPublication.latestForDiscovery | c747a256-6e0c-4969-b1bf-3b9f2f674289 | |
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