Understanding CO adsorption in MOFs combining atomic simulations and machine learning
Abstract 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 featu...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-76491-x |
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| author | Goktug Ercakir Gokhan Onder Aksu Seda Keskin |
| author_facet | Goktug Ercakir Gokhan Onder Aksu Seda Keskin |
| author_sort | Goktug Ercakir |
| collection | DOAJ |
| description | Abstract 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 Å), 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 ~ 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. |
| format | Article |
| id | doaj-art-eaa25d4477d645c8868a53f3864ef403 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eaa25d4477d645c8868a53f3864ef4032024-12-22T12:25:41ZengNature PortfolioScientific Reports2045-23222024-10-0114111210.1038/s41598-024-76491-xUnderstanding CO adsorption in MOFs combining atomic simulations and machine learningGoktug Ercakir0Gokhan Onder Aksu1Seda Keskin2Department of Chemical and Biological Engineering, Koç UniversityDepartment of Chemical and Biological Engineering, Koç UniversityDepartment of Chemical and Biological Engineering, Koç UniversityAbstract 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 Å), 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 ~ 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.https://doi.org/10.1038/s41598-024-76491-xMetal–organic framework (MOF)Carbon monoxide (CO)AdsorptionMolecular simulationMachine learning |
| spellingShingle | Goktug Ercakir Gokhan Onder Aksu Seda Keskin Understanding CO adsorption in MOFs combining atomic simulations and machine learning Scientific Reports Metal–organic framework (MOF) Carbon monoxide (CO) Adsorption Molecular simulation Machine learning |
| title | Understanding CO adsorption in MOFs combining atomic simulations and machine learning |
| title_full | Understanding CO adsorption in MOFs combining atomic simulations and machine learning |
| title_fullStr | Understanding CO adsorption in MOFs combining atomic simulations and machine learning |
| title_full_unstemmed | Understanding CO adsorption in MOFs combining atomic simulations and machine learning |
| title_short | Understanding CO adsorption in MOFs combining atomic simulations and machine learning |
| title_sort | understanding co adsorption in mofs combining atomic simulations and machine learning |
| topic | Metal–organic framework (MOF) Carbon monoxide (CO) Adsorption Molecular simulation Machine learning |
| url | https://doi.org/10.1038/s41598-024-76491-x |
| work_keys_str_mv | AT goktugercakir understandingcoadsorptioninmofscombiningatomicsimulationsandmachinelearning AT gokhanonderaksu understandingcoadsorptioninmofscombiningatomicsimulationsandmachinelearning AT sedakeskin understandingcoadsorptioninmofscombiningatomicsimulationsandmachinelearning |