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

ERC (European Research Council)HORIZON-ERCID: 101124002
EC Contribution
€20,000
Consortium Size
1 orgs
Start Year
2024
Summary

Metal organic frameworks (MOFs) are advanced porous materials with multifunctional tunable properties offering great potential for energy, environment, and biomedical technologies. The number of MOFs is increasing at an exponential rate. Studying millions of MOFs for different applications by random material selection using iterative experimental testing or brute-force computational simulations is impossible. The full potential of MOFs for target applications can only be unlocked if the storage and transport properties for important chemical and biological guest molecules trapped in the pores of each MOF are known. In this project, I will create a materials intelligence ecosystem for precisely assessing guest storage and transport properties of all MOFs by combining state-of-the-art atomistic calculations, molecular simulations, machine learning, and data science, integrated with past and future experiments. I will focus on ten critical guest molecules to address the key societal challenges of our world: hydrogen and methane to use MOFs for clean energy storage; ammonia, carbon monoxide, carbon dioxide, nitrous oxide to use MOFs for capturing toxic gas and combatting global warming; fluorouracil, methotrexate, nitrogen, oxygen to use MOFs as nanocarriers for anti-cancer drug therapy and biomedicine. The ground-breaking gains of my project will include the creation of the worlds first database for guest storage and transport properties of millions of MOFs; accurate assessments of new technologies by precise MOF-application matching; and generating design guidelines for high-performing MOFs to accelerate discovery of new materials. My novel methodology synergizing theory and data-driven science will greatly extend the reach of current experimental and computational studies by discovering new thermodynamic theories that will be extendible to other material classes and providing atomic-level insights into MOF-guest interactions that determine materials performances.

Consortium (1)

Project Results (15)

Source: CORDIS, the EU research results database.

Publications (15)
Diffusion explorer for the COF space: Data-driven discovery of high-performing COF membranes for gas separations
Carbon Capture Science & Technology· 2026DOI
Gokhan Onder Aksu; Seda Keskin
Leveraging molecular simulations and machine learning to assess CO2, O2, and N2 adsorption and separation performances of diverse MOF databases
Chemical Engineering Journal Advances· 2026DOI
Hasan Can Gulbalkan, Seda Keskin
ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning
Industrial & Engineering Chemistry Research· 2026DOI
Hilal Ozyurt, Gokhan Onder Aksu, Hasan Can Gulbalkan, Seda Keskin
Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research
Journal of the American Chemical Society· 2025DOI
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
Data‐Driven Design and Discovery of Metal–Organic Framework/Polymer Mixed Matrix Membranes
Macromolecular Materials and Engineering· 2025DOI
Seda Keskin
Industrial and Engineering Chemistry Research
Industrial & Engineering Chemistry Research· 2025DOI
Pelin Sezgin; Ezgi Gulcay-Ozcan; Marija Vučkovski; Aleksandra M. Bondžić; Ilknur Erucar; Seda Keskin
Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CH<sub>4</sub>/H<sub>2</sub> Separation
The Journal of Physical Chemistry C· 2025DOI
Pelin Sezgin; Seda Keskin
Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks
ACS Materials Au· 2025DOI
Pelin Sezgin; Feride Neva Yüngül; Beste Naz Karaca; Hasan Can Gulbalkan; Seda Keskin
Rational design of lanthanide-based metal–organic frameworks for CO<sub>2</sub> capture using computational modeling
Materials Advances· 2025DOI
Zeynep Pinar Haslak; Hasan Can Gulbalkan; Seda Keskin
The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning
ACS Materials Letters· 2025DOI
Gokhan Onder Aksu; Seda Keskin
The transformative role of machine learning in advancing MOF membranes for gas separations
Chemical Physics Reviews· 2025DOI
Pelin Sezgin; Seda Keskin
Advanced Materials
Advanced Materials· 2024DOI
Hilal Daglar; Hasan Can Gulbalkan; Gokhan Onder Aksu; Seda Keskin
Assessing Co2 Separation Performances of Il/Zif-8 Composites Using Molecular Features of Ils
Carbon Capture Science & Technology· 2024DOI
Hasan Can Gulbalkan; Alper Uzun; Seda Keskin
Finding high-performance MOFs for effective SF<sub>6</sub>/N<sub>2</sub> separation through high-throughput computational screening and machine learning
Journal of Physics: Materials· 2024DOI
Pelin Sezgin, Hasan Can Gulbalkan, Seda Keskin
Scientific Reports
Scientific Reports· 2024DOI
Goktug Ercakir; Gokhan Onder Aksu; Seda Keskin