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).

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