Research Project: Kovalent Organik Yapıların Doğal Gazı Karbondioksitten Arındırma Performanslarının Yüksek Çıktılı Bilgisayarlı Tarama Ve Makine Öğrenmesi Yöntemlerinin Bütünleştirilmesi İle Belirlenmesi
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Contributors
Funders
ID
TB.00690
Authors
Keskin, Seda
Faculty Member
Publications
Rapid and accurate screening of the COF space for natural gas purification: COFInformatics
(American Chemical Society, 2024) Aksu, Gökhan Önder; Keskin, Seda; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities;(ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups;and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.
Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning
(Royal Society of Chemistry, 2023) Keskin, Seda; Aksu, Gökhan Önder; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH4/H-2 separation. We studied 597 synthesized COFs for adsorption of a CH4/H-2 mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH4/H-2 selectivities, CH4 working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH4/H-2 separation using molecular simulations and the results showed that the top hypoCOFs have CH4 selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg(-1) under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH4/H-2 mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH4/H-2 mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH4/H-2 separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents.
