Research Project: Atomistic Modeling of Advanced Porous Materials for Energy, Environment, and Biomedical Applications
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Contributors
Funders
ID
EC.00198
Authors
Keskin, Seda
Faculty Member
Publications
Understanding CO adsorption in MOFs combining atomic simulations and machine learning
(Nature Portfolio, 2024) Keskin, Seda; Erçakır, Göktuğ; Aksu, Gökhan Önder; Department of Chemical and Biological Engineering; Yes; College of Engineering
This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized and hypothetical metal-organic frameworks (MOFs) at various pressures. After extracting structural, chemical, and energy-based features of the synthesized and hypothetical MOFs (hMOFs), we conducted molecular simulations to compute CO adsorption in synthesized MOFs and used these simulation results to train ML models for predicting CO adsorption in hMOFs. Results showed that CO uptakes of synthesized MOFs and hMOFs are between 0.02-2.28 mol/kg and 0.45-3.06 mol/kg, respectively, at 1 bar, 298 K. At low pressures (0.1 and 1 bar), Henry's constant of CO is the most dominant feature, whereas structural properties such as surface area and porosity are more influential for determining the CO uptakes of MOFs at high pressure (10 bar). Structural and chemical analyses revealed that MOFs with narrow pores (4.4-7.3 angstrom), aromatic ring-containing linkers and carboxylic acid groups, along with metal nodes such as Co, Zn, Ni achieve high CO uptakes at 1 bar. Our approach evaluated the CO uptakes of similar to 100,000 MOFs, the most extensive and diverse set studied for CO capture thus far, as a robust alternative to computationally demanding molecular simulations and iterative experiments.
Biomedical Applications of Metal-Organic Frameworks Revisited
(American Chemical Society, 2025) Sezgin, Pelin; Keskin, Seda; Gülcay-Özcan, Ezgi; Vuckovski, Marija; Bondzic, Aleksandra M.; Erucar, ilknur; Department of Chemical and Biological Engineering; Yes; Sezgin, Pelin; College of Engineering
Metal-organic frameworks (MOFs) have been shown to be great alternatives to traditional porous materials in various chemical applications, and they have been very widely studied for biomedical applications in the past decade specifically for drug storage. After our review published in 2011 [Keskin and K & imath;z & imath;lel, Ind. Eng. Chem. Res. 2011, 50 (4), 1799-1812, 10.1021/ie101312k], we have witnessed a very fast growth not only in the number and variety of MOFs but also in their usage across a broad spectrum of biomedical fields. With the recent integration of molecular modeling and data science approaches to the experimental studies, biomedical applications of MOFs have been significantly accelerated positioning them as pivotal components in the regenerative medicine, medical imaging, and diagnostics. In this review, we visited the diverse biomedical applications of MOFs considering the recent experimental and computational efforts on drug storage and delivery, bioimaging, and biosensing. We focused on the underlying mechanisms governing the molecular interactions between MOFs and biological systems and discussed both the opportunities and challenges in the field to highlight the potential of MOFs in advanced therapeutics for cancer and neurological diseases.
ReDD-COFFEE under the lens: revealing adsorption and separation performances of hypothetical COFs using molecular simulations and machine learning
(American Chemical Society, 2026) Keskin, Seda; Aksu, Gökhan Önder; Gülbalkan, Hasan Can; Özyurt, Hilal; Özyurt, Hilal; Gülbalkan, Hasan Can; Aksu, Gökhan Önder; Department of Chemical and Biological Engineering; Yes; College of Engineering
In this work, we performed a high-throughput computational screening approach combining Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML) to unlock the potential of the ReDD-COFFEE (Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field-based Energy Evaluation) database for gas adsorption and separation applications. Molecular simulations were first employed to assess CO2, CH4, H-2, N-2 and O-2 uptakes of acylhydrazone-, azine-, and triazine-based hypothetical COFs (hypoCOFs). These data were then leveraged to train ML models capable of predicting adsorption properties for nearly 25000 different types of materials. Adsorption selectivities of ReDD-hypoCOFs were computed for six important gas separations: CO2/CH4, CO2/H-2, CO2/N-2, CH4/H-2, CH4/N-2, and O-2/N-2. Structure-performance analyses performed using molecular fingerprinting on top-selective materials demonstrated that nitrogen enriched aromatic rings and fluorinated linkers in addition to narrow pores (<10 & Aring;) and low porosities (<0.7) collectively strengthen the CO2 affinity of ReDD-hypoCOFs.
Rational design of lanthanide-based metal-organic frameworks for CO2 capture using computational modeling
( Royal Society of Chemistry, 2025) Haşlak, Zeynep Pınar; Gülbalkan, Hasan Can; Keskin, Seda; Gülbalkan, Hasan Can; Department of Chemical and Biological Engineering; Yes; College of Engineering
Metal organic frameworks (MOFs) have emerged as promising materials in the context of CO2 capture and separation. Thanks to their tunable nature, various functionalities can be introduced to improve their separation performances. Lanthanide MOFs (Ln-MOFs) with high coordination numbers offer a promising space for the design of new high-performing and stable adsorbents for gas adsorption and separation. In this study, we combined molecular simulations with quantum mechanical (QM) calculations for designing new hypothetical materials offering superior CO2/N2 separation performances. An Ln-MOF having high CO2/N2 selectivity and working capacity was originally selected and its linkers were exchanged with five different types of linkers and its metal atom was exchanged with 12 different Ln3+ metals to generate 77 different types of hypothetic Ln-MOFs. Following the initial geometry optimizations at the molecular mechanics (MM) level, these structures were studied for CO2/N2 separation by performing grand canonical Monte Carlo (GCMC) simulations. Five MOFs were found to outperform the original Ln-MOF structure and they were optimized at the QM level to obtain geometries with minimized total energy, which finally led to two hypothetic Ln-MOFs offering superior CO2/N2 separation performance. The computational work that we described in this study will be useful for the rational design of new Ln-based MOFs with improved CO2 separation properties.
The COF space: materials features, gas adsorption, and separation performances assessed by machine learning
(American Chemical Society, 2025) Aksu, Gökhan Önder; Keskin, Seda; Aksu, Gökhan Önder; Department of Chemical and Biological Engineering; Yes; College of Engineering
Covalent organic frameworks (COFs) are promising materials for gas adsorption; however, only a small number of COFs has been studied for a few types of gas separations to date. To unlock the full potential of the COF space, composed of 69 784 different types of materials, we studied the adsorption of five important gas molecules, CO2, CH4, H2, N2, and O2 in COFs at various pressures combining high-throughput molecular simulations and machine learning. Adsorbent performances of COFs were then explored for industrially critical separations, such as CO2/CH4, CO2/H2, CO2/N2, CH4/H2, CH4/N2, and O2/N2. The key structural and chemical properties of the most promising adsorbents were revealed. Our work offers the most extensive dataset produced for COFs in the literature composed of similar to 4.3 million data points for all synthesized and hypothetical COFs' structural, chemical, and energetic features; gas adsorption properties; and selectivities to facilitate the materials discovery.
