Research Project:
Atomistic Modeling of Advanced Porous Materials for Energy, Environment, and Biomedical Applications

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dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-12-29T10:38:02Z
dc.date.available2024-12-29
dc.description.publisherscopeInternational
dc.identifierEC.00198
dc.identifier.urihttps://hdl.handle.net/20.500.14288/24238
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Projects Collection
dc.relation.relatedpublicationsUnderstanding CO adsorption in MOFs combining atomic simulations and machine learning
dc.relation.relatedpublicationsBiomedical Applications of Metal-Organic Frameworks Revisited
dc.relation.relatedpublicationsReDD-COFFEE under the lens: revealing adsorption and separation performances of hypothetical COFs using molecular simulations and machine learning
dc.relation.relatedpublicationsRational design of lanthanide-based metal-organic frameworks for CO2 capture using computational modeling
dc.relation.relatedpublicationsThe COF space: materials features, gas adsorption, and separation performances assessed by machine learning
dc.relation.relatedpublicationsData-driven design and discovery of metal-organic framework/polymer mixed matrix membranes
dc.relation.relatedpublicationsThe transformative role of machine learning in advancing MOF membranes for gas separations
dc.relation.relatedpublicationsDiffusion explorer for the COF space: data-driven discovery of high-performing COF membranes for gas separations
dc.relation.relatedpublicationsComputational simulations of metal–organic frameworks to enhance adsorption applications
dc.relation.relatedpublicationsAssessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs
dc.relation.relatedpublicationsMolecular modeling-based machine learning for accurate prediction of gas diffusivity and permeability in metal–organic frameworks
dc.relation.relatedpublicationsArtificial intelligence paradigms for next-generation metal−organic framework research
dc.relation.relatedpublicationsLeveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases
dc.relation.relatedpublicationsIntegrating molecular simulations with machine learning to discover selective MOFs for CH4/H2 separation
dc.titleAtomistic Modeling of Advanced Porous Materials for Energy, Environment, and Biomedical Applications
dc.title.alternativeSTARLET
dspace.entity.typeProject
local.contributor.kuauthorKeskin, Seda
local.project.contractnumber101124002
local.project.endDate2029-03-30
local.project.startDate2024-03-31
local.project.templateEC.HE.EXCSCI.ERCCOG
project.funder.identifierEC-H2020 EUROPEAN COMMISSION
project.funder.nameEuropean Commission
project.investigatorKeskin, Seda
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