Welcome to COSMOS!

COSMOS (Computational Simulations of MOFs for Gas Separations) is an ERC-2017-Starting Grant. ERC grantee is Prof. Seda Keskin Avci at Koc University. The main objective of COSMOS is to obtain the fundamental, atomic-level insights into the CO2 separation performances of metal organic frameworks (MOFs). This webpage is designed to share the results of COSMOS.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 756489).

Merging machine learning with computer simulations to discover MOFs and COFs

Our group has recently combined computer simulations with machine learning approaches to screen large number of MOFs, COFs, and IL/MOFs composite for various gas separations. Several of our publications in the field are as follows:

Daglar H., Keskin S. “Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs“ ACS Applied Materials & Interfaces 14, 32134–32148  (2022).

Demir H. and Keskin S. “Revealing Acetylene Separation Performances of Anion-Pillared MOFs by Combining Molecular Simulations and Machine Learning” Chemical Engineering Journal 464, 142731 (2023).

Daglar H., Gulbalkan H., Nitasha H., Durak O., Uzun A., Keskin S. “Integrating Molecular Simulations with Machine Learning Guides the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation” ACS Applied Materials & Interfaces 15, 17421–17431 (2023).

Demir H., Daglar H., Gulbalkan H., Aksu O., Keskin S. “Recent Advances in Computational Modeling of MOFs: From Molecular Simulations to Machine Learning” Coordination Chemistry Reviews 484, 215112 (2023).

Altintas C. and Keskin S. “On the Shoulders of High-Throughput Computational Screening and Machine Learning: Design and Discovery of MOFs for H2 Storage and Purification” Materials Today Energy 38, 101426 (2023).

Demir H. and Keskin S. “A New Era of Modeling MOF-Based Membranes: Cooperation of Theory and Data Science” Macromolecular Materials and Engineering 309, 2300225 (2024).

Aksu G.O. and Keskin S. “Advancing CH4/H2 Separation with Covalent Organic Frameworks by Combining Molecular Simulations and Machine Learning” Journal of Materials Chemistry A 11, 14788-14799  (2023).

Combined experiments and simulations to understand MOF adsorbents and membranes

Zeeshan M., Gulbalkan H.C., Durak O., Haslak Z.P., Unal U., Keskin S., Uzun A. “An Integrated Computational–Experimental Hierarchical Approach for the Rational Design of an IL/UiO-66 Composite Offering Infinite CO2 Selectivity“ Advanced Functional Materials 32, 2204149 (2022).

Habib N.,  Durak O.,  Zeeshan M., Uzun A., Keskin S. “A Novel IL/MOF/Polymer Mixed Matrix Membrane having Superior CO2/N2 Selectivity” Journal of Membrane Science 658, 120712 (2022).

Habib N., Durak O., Uzun A. Keskin S. “Incorporation of a pyrrolidinium-based ionic liquid/MIL-101 (Cr) composite into Pebax sets a new benchmark for CO2/N2 selectivity” Separation Purification Technology 312, 123346 (2023).

Our recent publications of computer simulations of MOFs and COFs

Computational Modeling of COFs

“Multi-Scale Computational Screening to Accelerate Discovery of IL/COF Composites for CO2/N2 Separation“ Separation and Purification Technology 287, 120578 (2022).

“Accelerating Discovery of COFs for CO2 Capture and H2 Purification Using Structurally Guided Computational Screening” Chemical Engineering Journal 427, 131574 (2022).

“Combined GCMC, MD, and DFT Approach for Unlocking the Performances of COFs for Methane Purification” Industrial & Engineering Chemistry Research 60 (35) 12999–13012 (2021). 

Computational Modeling of MOFs for Various Gas Separations

Several of our works on computational modeling of MOFs for gas separations have been recently published:

Altintas C., Keskin S. “MOF Adsorbents for Flue Gas Separation Comparison of Material Ranking Approaches“ Chemical Engineering Research and Design 179, 308-318 (2022).

Gulbalkan H., Haslak P., Altintas C., Uzun A.,  Keskin S. “Assessing CH4/N2 Separation Potential of MOFs, COFs, IL/MOF, MOF/Polymer, and COF/Polymer Composites” Chemical Engineering Journal 428, 131239 (2022).

Daglar H., Aydin S., Keskin S. “MOF-based MMMs breaking the upper bounds of polymers for a large variety of gas separations“ Separation and Purification Technology  281, 119811 (2022).

Avci G., Altintas C., Keskin S. “Metal Exchange Boosts the CO2 Selectivity of MOFs having Zn-oxide Node” Journal of Physical Chemistry C 125, 31, 17311–17322 (2021).

Daglar H., Erucar I.,  Keskin S. “Recent Advances in Simulating Gas Permeation through MOF Membranes” Materials Advances  2, 5300-5317 (2021).

Demir H., Keskin S. “Computational Insights into Efficient CO2 and H2S Capture Through Zirconium MOFs“ Journal of CO2 Utilization 55, 101811 (2021).

Demir H., Keskin S. “Zr-MOFs for CF4/CH4, CH4/H2, and CH4/N2 Separation: Towards the Goal of Discovering Stable and Effective Adsorbents” Molecular Systems Design & Engineering  6, 627-642  (2021).

Altintas C., Altundal O.F., Keskin S., Yildirim R. “Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation” Journal of Chemical Information and Modeling 61, 2146-2131 (2021).

Selection of MOF Databases

Our new paper on the selection of MOF databases for computational assessment of MOFs is now available: “Effect of MOF Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs” Angewandte Chemie 60, 7828-7837 (2021). Here is the link.

Design of MOF Composites for Gas Separations

Our new papers on the experimental synthesis of several MOF composites for gas separations are now available:

“Doubling CO2/N2 separation performance of CuBTC by incorporation of 1-n-ethyl-3-methylimidazolium diethyl phosphate” Microporous Mesoporous Materials 316, Article Number: 110947(2021). Here is the link.

“Composites of porous materials with ionic liquids: Synthesis, characterization, applications, and beyond“ Microporous and Mesoporous Materials 332, 11703 (2022). Here is the link.