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

Project Information

STARLET

Grant agreement ID: 101124002

DOI

10.3030/101124002

EC signature date 31 January 2024

Start date 1 April 2024

End date 31 March 2029

Funded under

• European Research Council (ERC)

Total cost

€ 2 000 000,00

EU contribution

€ 2 000 000,00

Coordinated by

KOC UNIVERSITY
Türkiye

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Peer reviewed articles (21)

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

Author(s): Pelin Sezgin; Seda Keskin
Published in: Chemical Physics Reviews, 2025, ISSN 2688-4070
Publisher: AIP
DOI: 10.1063/5.0278371

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

Author(s): Gokhan Onder Aksu; Seda Keskin
Published in: ACS Materials Letters, 2025, ISSN 2639-4979
Publisher: AMER CHEMICAL SOC
DOI: 10.1021/ACSMATERIALSLETT.4C02594

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

Author(s): 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
Published in: Journal of the American Chemical Society, 2025, ISSN 1520-5126
Publisher: 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

Author(s): Pelin Sezgin, Hasan Can Gulbalkan, Seda Keskin
Published in: Journal of Physics: Materials, Issue 7, 2024, ISSN 2515-7639
Publisher: IOP Publishing
DOI: 10.1088/2515-7639/AD80CD

Gaining molecular insights into structure-performance relations of MOFs via high-throughput computational screening

Author(s): Seda Keskin
Published in: Microporous and Mesoporous Materials, Issue 409, 2026, ISSN 1387-1811
Publisher: Elsevier BV
DOI: 10.1016/J.MICROMESO.2026.114180

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

Author(s): Pelin Sezgin; Feride Neva Yüngül; Beste Naz Karaca; Hasan Can Gulbalkan; Seda Keskin
Published in: ACS Materials Au, 2025, ISSN 2694-2461
Publisher: AMER CHEMICAL SOC
DOI: 10.1021/ACSMATERIALSAU.5C00111

Advanced Materials

Author(s): Hilal Daglar; Hasan Can Gulbalkan; Gokhan Onder Aksu; Seda Keskin
Published in: Advanced Materials, 2024, ISSN 0935-9648
Publisher: 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

Author(s): Zeynep Pinar Haslak; Hasan Can Gulbalkan; Seda Keskin
Published in: Materials Advances, 2025, ISSN 2633-5409
Publisher: ROYAL SOC CHEMISTRY
DOI: 10.1039/D5MA00017C

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

Author(s): Seda Keskin
Published in: Macromolecular Materials and Engineering, 2025, ISSN 1439-2054
Publisher: Wiley
DOI: 10.1002/MAME.202500364

Scientific Reports

Author(s): Goktug Ercakir; Gokhan Onder Aksu; Seda Keskin
Published in: Scientific Reports, 2024, ISSN 2045-2322
Publisher: NATURE PORTFOLIO
DOI: 10.1038/S41598-024-76491-X

The Materials Database Plateau: What Comes After Millions of Materials?

Author(s): Seda Keskin
Published in: ACS Materials Letters, 2026, ISSN 2639-4979
Publisher: American Chemical Society (ACS)
DOI: 10.1021/ACSMATERIALSLETT.6C00925

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

Author(s): Hasan Can Gulbalkan; Alper Uzun; Seda Keskin
Published in: Carbon Capture Science & Technology, 2024, ISSN 2772-6568
Publisher: Elsevier
DOI: 10.1016/J.CCST.2025.100373

Discovering metal-organic framework/polymer mixed-matrix membranes via machine learning for CO2 separation

Author(s): Feride Neva Yüngül, Seda Keskin
Published in: Communications Materials, Issue 7, 2026, ISSN 2662-4443
Publisher: Springer Science and Business Media LLC
DOI: 10.1038/S43246-026-01207-9

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

Author(s): Gokhan Onder Aksu; Seda Keskin
Published in: Carbon Capture Science & Technology, 2026, ISSN 2772-6568
Publisher: 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

Author(s): Hilal Ozyurt, Gokhan Onder Aksu, Hasan Can Gulbalkan, Seda Keskin
Published in: Industrial & Engineering Chemistry Research, Issue 65, 2026, ISSN 0888-5885
Publisher: American Chemical Society (ACS)
DOI: 10.1021/ACS.IECR.5C04806

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

Author(s): Pelin Sezgin; Seda Keskin
Published in: The Journal of Physical Chemistry C, 2025, ISSN 1932-7455
Publisher: AMER CHEMICAL SOC
DOI: 10.1021/ACS.JPCC.5C02779

Large‐Scale Screening of Covalent Organic Framework/Polymer Mixed‐Matrix Membranes for Advanced Gas Separations

Author(s): Feride Neva Yungul, Gokhan Onder Aksu, Seda Keskin
Published in: Macromolecular Materials and Engineering, Issue 311, 2026, ISSN 1438-7492
Publisher: Wiley
DOI: 10.1002/MAME.70303

Industrial and Engineering Chemistry Research

Author(s): Pelin Sezgin; Ezgi Gulcay-Ozcan; Marija Vučkovski; Aleksandra M. Bondžić; Ilknur Erucar; Seda Keskin
Published in: Industrial & Engineering Chemistry Research, 2025, ISSN 0888-5885
Publisher: 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

Author(s): Hasan Can Gulbalkan, Seda Keskin
Published in: Chemical Engineering Journal Advances, Issue 25, 2026, ISSN 2666-8211
Publisher: Elsevier BV
DOI: 10.1016/J.CEJA.2025.100984

Machine learning-accelerated discovery of metal–organic frameworks for storage and delivery of anticancer drug molecules

Author(s): Hasan Can Gulbalkan, Seda Keskin
Published in: Digital Discovery, Issue 5, 2026, ISSN 2635-098X
Publisher: Royal Society of Chemistry (RSC)
DOI: 10.1039/D6DD00223D

Digital ReticularChemistry: How Artificial Intelligenceis Redefining Covalent Organic Framework Research

Author(s): Gokhan Onder Aksu, Seda Keskin
Published in: JACS Au, Issue 6, 2026, ISSN 2691-3704
Publisher: American Chemical Society (ACS)
DOI: 10.1021/JACSAU.6C00576