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

Información del proyecto

STARLET

Identificador del acuerdo de subvención: 101124002

DOI

10.3030/101124002

Fecha de la firma de la CE 31 Enero 2024

Fecha de inicio 1 Abril 2024

Fecha de finalización 31 Marzo 2029

Financiado con arreglo a

• European Research Council (ERC)

Coste total

€ 2 000 000,00

Aportación de la UE

€ 2 000 000,00

Coordinado por

KOC UNIVERSITY
Türkiye

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Publicaciones

Peer reviewed articles (15)

The transformative role of machine learning in advancing MOF membranes for gas separations

Autores: Pelin Sezgin; Seda Keskin
Publicado en: Chemical Physics Reviews, 2025, ISSN 2688-4070
Editor: AIP
DOI: 10.1063/5.0278371

The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning

Autores: Gokhan Onder Aksu; Seda Keskin
Publicado en: ACS Materials Letters, 2025, ISSN 2639-4979
Editor: AMER CHEMICAL SOC
DOI: 10.1021/ACSMATERIALSLETT.4C02594

Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research

Autores: Aydin Ozcan; François-Xavier Coudert; Sven M. J. Rogge; Greta Heydenrych; Dong Fan; Antonios P. Sarikas; Seda Keskin; Guillaume Maurin; George E. Froudakis; Stefan Wuttke; Ilknur Erucar
Publicado en: Journal of the American Chemical Society, 2025, ISSN 1520-5126
Editor: AMER CHEMICAL SOC
DOI: 10.1021/JACS.5C08214

Finding high-performance MOFs for effective SF₆/N₂ separation through high-throughput computational screening and machine learning

Autores: Pelin Sezgin, Hasan Can Gulbalkan, Seda Keskin
Publicado en: Journal of Physics: Materials, Edición 7, 2024, ISSN 2515-7639
Editor: IOP Publishing
DOI: 10.1088/2515-7639/AD80CD

Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks

Autores: Pelin Sezgin; Feride Neva Yüngül; Beste Naz Karaca; Hasan Can Gulbalkan; Seda Keskin
Publicado en: ACS Materials Au, 2025, ISSN 2694-2461
Editor: AMER CHEMICAL SOC
DOI: 10.1021/ACSMATERIALSAU.5C00111

Advanced Materials

Autores: Hilal Daglar; Hasan Can Gulbalkan; Gokhan Onder Aksu; Seda Keskin
Publicado en: Advanced Materials, 2024, ISSN 0935-9648
Editor: WILEY-V C H VERLAG GMBH
DOI: 10.1002/ADMA.202405532

Rational design of lanthanide-based metal–organic frameworks for CO₂ capture using computational modeling

Autores: Zeynep Pinar Haslak; Hasan Can Gulbalkan; Seda Keskin
Publicado en: Materials Advances, 2025, ISSN 2633-5409
Editor: ROYAL SOC CHEMISTRY
DOI: 10.1039/D5MA00017C

Data‐Driven Design and Discovery of Metal–Organic Framework/Polymer Mixed Matrix Membranes

Autores: Seda Keskin
Publicado en: Macromolecular Materials and Engineering, 2025, ISSN 1439-2054
Editor: Wiley
DOI: 10.1002/MAME.202500364

Scientific Reports

Autores: Goktug Ercakir; Gokhan Onder Aksu; Seda Keskin
Publicado en: Scientific Reports, 2024, ISSN 2045-2322
Editor: NATURE PORTFOLIO
DOI: 10.1038/S41598-024-76491-X

Assessing Co2 Separation Performances of Il/Zif-8 Composites Using Molecular Features of Ils

Autores: Hasan Can Gulbalkan; Alper Uzun; Seda Keskin
Publicado en: Carbon Capture Science & Technology, 2024, ISSN 2772-6568
Editor: Elsevier
DOI: 10.1016/J.CCST.2025.100373

Diffusion explorer for the COF space: Data-driven discovery of high-performing COF membranes for gas separations

Autores: Gokhan Onder Aksu; Seda Keskin
Publicado en: Carbon Capture Science & Technology, 2026, ISSN 2772-6568
Editor: Elsevier
DOI: 10.1016/J.CCST.2025.100559

ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning

Autores: Hilal Ozyurt, Gokhan Onder Aksu, Hasan Can Gulbalkan, Seda Keskin
Publicado en: Industrial & Engineering Chemistry Research, Edición 65, 2026, ISSN 0888-5885
Editor: American Chemical Society (ACS)
DOI: 10.1021/ACS.IECR.5C04806

Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CH₄/H₂ Separation

Autores: Pelin Sezgin; Seda Keskin
Publicado en: The Journal of Physical Chemistry C, 2025, ISSN 1932-7455
Editor: AMER CHEMICAL SOC
DOI: 10.1021/ACS.JPCC.5C02779

Industrial and Engineering Chemistry Research

Autores: Pelin Sezgin; Ezgi Gulcay-Ozcan; Marija Vučkovski; Aleksandra M. Bondžić; Ilknur Erucar; Seda Keskin
Publicado en: Industrial & Engineering Chemistry Research, 2025, ISSN 0888-5885
Editor: AMER CHEMICAL SOC
DOI: 10.1021/ACS.IECR.4C03698

Leveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases

Autores: Hasan Can Gulbalkan, Seda Keskin
Publicado en: Chemical Engineering Journal Advances, Edición 25, 2026, ISSN 2666-8211
Editor: Elsevier BV
DOI: 10.1016/J.CEJA.2025.100984