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Xin R, Wang C, Zhang Y, Peng R, Li R, Wang J, Mao Y, Zhu X, Zhu W, Kim M, Nam HN, Yamauchi Y. Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space. ACS NANO 2024. [PMID: 38951518 DOI: 10.1021/acsnano.4c04174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.
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Affiliation(s)
- Ruiqi Xin
- College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Chaohai Wang
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Yingchao Zhang
- School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450000, China
| | - Rongfu Peng
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Rui Li
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
- College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
| | - Junning Wang
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Yanli Mao
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Xinfeng Zhu
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, Henan International Joint Laboratory for Green Low Carbon-Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, China
| | - Wenkai Zhu
- College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China
| | - Minjun Kim
- School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Ho Ngoc Nam
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
| | - Yusuke Yamauchi
- School of Chemical Engineering and Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland 4072, Australia
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
- Department of Plant and Environmental New Resources, College of Life Sciences, Kyung Hee University, Gyeonggi-do, 17104, South Korea
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Ercakir G, Aksu GO, Keskin S. High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures. J Chem Phys 2024; 160:084706. [PMID: 38415834 DOI: 10.1063/5.0189493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024] Open
Abstract
In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10 Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.
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Affiliation(s)
- Goktug Ercakir
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Gokhan Onder Aksu
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Seda Keskin
- Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
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Yu X, Tang D, Chng JY, Sholl DS. Efficient Exploration of Adsorption Space for Separations in Metal-Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2023; 127:19229-19239. [PMID: 37791097 PMCID: PMC10544990 DOI: 10.1021/acs.jpcc.3c04533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/15/2023] [Indexed: 10/05/2023]
Abstract
Adsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. To enable exploration of a wider adsorption space, we carefully selected a diverse set of 45 molecules and 335 MOFs and generated single-component isotherms of 15,075 MOF-molecule pairs by grand canonical Monte Carlo. Using this database, we successfully developed accurate (r2 > 0.9) machine learning models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. With this approach, we can efficiently make predictions of large collections of MOFs for arbitrary mixture separations. By combining molecular simulation data and ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures.
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Affiliation(s)
- Xiaohan Yu
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Dai Tang
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jia Yuan Chng
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - David S. Sholl
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Oak
Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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Li L, Zhao Y, Yu H, Wang Z, Zhao Y, Jiang M. An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:6756-6766. [PMID: 37130050 DOI: 10.1021/acs.langmuir.3c00255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal-organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.
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Affiliation(s)
- Lujun Li
- Department of Automation, University of Science and Technology of China, Hefei 230026, China
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Yiming Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Haibin Yu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Zhuo Wang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Yongjia Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Mingqi Jiang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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A neural recommender system for efficient adsorbent screening. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Affiliation(s)
- Josiah Roberts
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
| | - Eva Zurek
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, USA
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Fanourgakis GS, Gkagkas K, Froudakis G. Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage. J Chem Phys 2022; 156:054103. [DOI: 10.1063/5.0075994] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- George S. Fanourgakis
- Department of Chemistry, University of Crete, Voutes Campus, GR-70013 Heraklion, Crete, Greece
| | - Konstantinos Gkagkas
- Material Engineering Division, Toyota Motor Europe NV/SA, Technical Center, Hoge Wei 33B, 1930 Zaventem, Belgium
| | - George Froudakis
- Department of Chemistry, University of Crete, Voutes Campus, GR-70013 Heraklion, Crete, Greece
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Hypothetical yet Effective: Computational Identification of High-performing MOFs for CO2 Capture. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Gharagheizi F, Sholl DS. Comprehensive Assessment of the Accuracy of the Ideal Adsorbed Solution Theory for Predicting Binary Adsorption of Gas Mixtures in Porous Materials. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c03876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Farhad Gharagheizi
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
| | - David S. Sholl
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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