1
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Shen J, Kumar A, Wahiduzzaman M, Barpaga D, Maurin G, Motkuri RK. Engineered Nanoporous Frameworks for Adsorption Cooling Applications. Chem Rev 2024; 124:7619-7673. [PMID: 38683669 DOI: 10.1021/acs.chemrev.3c00450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population's need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. This review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF-refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent-refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.
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Affiliation(s)
- Jian Shen
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- College of Environment and Resources, Xiangtan University, Xiangtan 411105, P.R. China
| | - Abhishek Kumar
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | | | - Dushyant Barpaga
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Guillaume Maurin
- ICGM, University of Montpellier, CNRS, ENSCM, 34293 Montpellier, France
| | - Radha Kishan Motkuri
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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2
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Kumar A, Pant KK, Upadhyayula S, Kodamana H. Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models. ACS OMEGA 2023; 8:410-421. [PMID: 36643461 PMCID: PMC9835089 DOI: 10.1021/acsomega.2c04919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
Methanol production has gained considerable interest on the laboratory and industrial scale as it is a renewable fuel and an excellent hydrogen energy storehouse. The formation of synthesis gas (CO/H2) and the conversion of synthesis gas to methanol are the two basic catalytic processes used in methanol production. Machine learning (ML) approaches have recently emerged as powerful tools in reaction informatics. Inspired by these, we employ Gaussian process regression (GPR) to the model conversion of carbon monoxide (CO) and selectivity of the methanol product using data sets obtained from experimental investigations to capture uncertainty in prediction values. The results indicate that the proposed GPR model can accurately predict CO conversion and methanol selectivity as compared to other ML models. Further, the factors that influence the predictions are identified from the best GPR model employing "Shapley Additive exPlanations" (SHAP). After interpretation, the essential input features are found to be the inlet mole fraction of CO (Y(CO, in)) and the net inlet flow rate (Fin(nL/min)) for our best prediction GPR models, irrespective of our data sets. These interpretable models are employed for Bayesian optimization in a weighted multiobjective framework to obtain the optimal operating points, namely, maximization of both selectivity and conversion.
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Affiliation(s)
- Avan Kumar
- Department
of Chemical Engineering, Indian Institute
of Technology Delhi, Hauz Khas, New Delhi110016, India
| | - Kamal K. Pant
- Department
of Chemical Engineering, Indian Institute
of Technology Delhi, Hauz Khas, New Delhi110016, India
| | - Sreedevi Upadhyayula
- Department
of Chemical Engineering, Indian Institute
of Technology Delhi, Hauz Khas, New Delhi110016, India
| | - Hariprasad Kodamana
- Department
of Chemical Engineering, Indian Institute
of Technology Delhi, Hauz Khas, New Delhi110016, India
- Yardi
School of Artificial Intelligence, Indian
Institute of Technology Delhi, Hauz Khas, New Delhi110016, India
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3
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Mel’gunov MS. Application of the simple Bayesian classifier for the N2 (77 K) adsorption/desorption hysteresis loop recognition. ADSORPTION 2022. [DOI: 10.1007/s10450-022-00369-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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4
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Zhao L, Zhang Q, He C, Chen Q, Zhang BJ. Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels. ACS OMEGA 2022; 7:33895-33907. [PMID: 36188274 PMCID: PMC9520561 DOI: 10.1021/acsomega.2c02779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
This work is devoted to the development of quantitative structure-property relationship (QSPR) models using various regression analyses to predict propylene (C3H6) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C3H6 uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C3H6 uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (Q st) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C3H6 adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications.
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5
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Lu X, Xie Z, Wu X, Li M, Cai W. Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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6
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Li H, Wang C, Zeng Y, Li D, Yan Y, Zhu X, Qiao Z. Combining Computational Screening and Machine Learning to Predict Metal-Organic Framework Adsorbents and Membranes for Removing CH 4 or H 2 from Air. MEMBRANES 2022; 12:830. [PMID: 36135849 PMCID: PMC9503901 DOI: 10.3390/membranes12090830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Separating and capturing small amounts of CH4 or H2 from a mixture of gases, such as coal mine spent air, at a large scale remains a great challenge. We used large-scale computational screening and machine learning (ML) to simulate and explore the adsorption, diffusion, and permeation properties of 6013 computation-ready experimental metal-organic framework (MOF) adsorbents and MOF membranes (MOFMs) for capturing clean energy gases (CH4 and H2) in air. First, we modeled the relationships between the adsorption and the MOF membrane performance indicators and their characteristic descriptors. Among three ML algorithms, the random forest was found to have the best prediction efficiency for two systems (CH4/(O2 + N2) and H2/(O2 + N2)). Then, the algorithm was further applied to quantitatively analyze the relative importance values of seven MOF descriptors for five performance metrics of the two systems. Furthermore, the 20 best MOFs were also selected. Finally, the commonalities between the high-performance MOFs were analyzed, leading to three types of material design principles: tuned topology, alternative metal nodes, and organic linkers. As a result, this study provides microscopic insights into the capture of trace amounts of CH4 or H2 from air for applications involving coal mine spent air and hydrogen leakage.
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Affiliation(s)
- Huilin Li
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Cuimiao Wang
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yue Zeng
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Dong Li
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yaling Yan
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Xin Zhu
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
| | - Zhiwei Qiao
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
- Joint Institute of Guangzhou University & Institute of Corrosion Science and Technology, Guangzhou University, Guangzhou 510006, China
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7
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Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas. MEMBRANES 2022; 12:membranes12070700. [PMID: 35877903 PMCID: PMC9321510 DOI: 10.3390/membranes12070700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022]
Abstract
To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO2 from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO2, N2, and O2) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.
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8
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Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107739] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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9
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Pan Y, He L, Ren Y, Wang W, Wang T. Analysis of Influencing Factors on the Gas Separation Performance of Carbon Molecular Sieve Membrane Using Machine Learning Technique. MEMBRANES 2022; 12:membranes12010100. [PMID: 35054626 PMCID: PMC8778672 DOI: 10.3390/membranes12010100] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/16/2022]
Abstract
Gas separation performance of the carbon molecular sieve (CMS) membrane is influenced by multiple factors including the microstructural characteristics of carbon and gas properties. In this work, the support vector regression (SVR) method as a machine learning technique was applied to the correlation between the gas separation performance, the multiple membrane structure, and gas characteristic factors of the self-manufactured CMS membrane. A simple quantitative index based on the Robeson’s upper bound line, which indicated the gas permeability and selectivity simultaneously, was proposed to measure the gas separation performance of CMS membrane. Based on the calculation results, the inferred key factors affecting the gas permeability of CMS membrane were the fractional free volume (FFV) of the precursor, the average interlayer spacing of graphite-like carbon sheet, and the final carbonization temperature. Moreover, the most influential factors for the gas separation performance were supposed to be the two structural factors of precursor influencing the porosity of CMS membrane, the carbon residue and the FFV, and the ratio of the gas kinetic diameters. The results would be helpful to the structural optimization and the separation performance improvement of CMS membrane.
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Affiliation(s)
- Yanqiu Pan
- School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China; (Y.P.); (L.H.); (T.W.)
| | - Liu He
- School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China; (Y.P.); (L.H.); (T.W.)
- Jihua Laboratory, Foshan 528000, China
| | - Yisu Ren
- Faculty of Science, The University of Melbourne, Melbourne, VIC 3010, Australia;
| | - Wei Wang
- School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China; (Y.P.); (L.H.); (T.W.)
- Correspondence:
| | - Tonghua Wang
- School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China; (Y.P.); (L.H.); (T.W.)
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10
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Li L, Shi Z, Liang H, Liu J, Qiao Z. Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:159. [PMID: 35010109 PMCID: PMC8746952 DOI: 10.3390/nano12010159] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/29/2021] [Accepted: 12/31/2021] [Indexed: 12/10/2022]
Abstract
Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.
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Affiliation(s)
- Lifeng Li
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; (L.L.); (Z.S.)
| | - Zenan Shi
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; (L.L.); (Z.S.)
| | - Hong Liang
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; (L.L.); (Z.S.)
| | - Jie Liu
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan 430073, China
| | - Zhiwei Qiao
- Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; (L.L.); (Z.S.)
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11
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Predicting adsorption and separation performance indicators of Xe/Kr in metal-organic frameworks via a precursor-based neural network model. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Li Z, Bucior BJ, Chen H, Haranczyk M, Siepmann JI, Snurr RQ. Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures. J Chem Phys 2021; 155:014701. [PMID: 34241399 DOI: 10.1063/5.0050823] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.
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Affiliation(s)
- Zhao Li
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Benjamin J Bucior
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Haoyuan Chen
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
| | - Maciej Haranczyk
- IMDEA Materials Institute, C/Eric Kandel 2, Getafe 28906, Madrid, Spain
| | - J Ilja Siepmann
- Department of Chemistry and Chemical Theory Center, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, USA
| | - Randall Q Snurr
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA
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13
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Molecular fingerprint and machine learning to accelerate design of
high‐performance
homochiral metal–organic frameworks. AIChE J 2021. [DOI: 10.1002/aic.17352] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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14
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Altintas C, Altundal OF, Keskin S, Yildirim R. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation. J Chem Inf Model 2021; 61:2131-2146. [PMID: 33914526 PMCID: PMC8154255 DOI: 10.1021/acs.jcim.1c00191] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Indexed: 02/06/2023]
Abstract
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.
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Affiliation(s)
- Cigdem Altintas
- Department
of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
| | - Omer Faruk Altundal
- 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
| | - Ramazan Yildirim
- Department
of Chemical Engineering, Boğaziçi
University, Bebek, 34342 Istanbul, Turkey
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15
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Mukherjee K, Colón YJ. Machine learning and descriptor selection for the computational discovery of metal-organic frameworks. MOLECULAR SIMULATION 2021. [DOI: 10.1080/08927022.2021.1916014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Krishnendu Mukherjee
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Yamil J. Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
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16
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Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks. Sci Rep 2021; 11:8888. [PMID: 33903606 PMCID: PMC8076181 DOI: 10.1038/s41598-021-88027-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/06/2021] [Indexed: 12/05/2022] Open
Abstract
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.
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17
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Tang D, Gharagheizi F, Sholl DS. Adsorption-Based Separation of Near-Azeotropic Mixtures-A Challenging Example for High-Throughput Development of Adsorbents. J Phys Chem B 2021; 125:926-936. [PMID: 33448857 DOI: 10.1021/acs.jpcb.0c10764] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Adsorption of gas mixtures is central to adsorption-based gas separations, and the number of adsorbate mixture/adsorbent systems that exist is staggering. Because examples of machine learning (ML) models predicting single-component adsorption of arbitrary molecules in large libraries of crystalline adsorbents have been developed, it is interesting to determine whether these models can accurately predict mixture adsorption. Here, we use molecular simulations to generate mixture adsorption data with a set of 12 near-azeotropic molecules in a diverse set of MOFs. These data provide a challenging example for any method to rapidly predict mixture adsorption in MOFs. We combine a previous ML single-component isotherm model with ideal adsorbed solution theory (IAST) to make predictions that can be compared directly with molecular simulation data for these adsorbed mixtures. This combination of ML and IAST illustrates the scope that is available with these methods, but the accuracy of the resulting predictions is disappointing. By examining the same examples with IAST based on minimal molecular simulation data for single-component isotherms, we show that having an accurate description of adsorption in the dilute loading limit is critical to being able to accurately predict mixture adsorption. This observation points to a useful direction for future work developing robust ML models of adsorption isotherms for diverse collections of molecules and adsorbents.
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Affiliation(s)
- Dai Tang
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0100, United States
| | - 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
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18
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Long R, Xia X, Zhao Y, Li S, Liu Z, Liu W. Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning. iScience 2021; 24:101914. [PMID: 33385115 PMCID: PMC7772570 DOI: 10.1016/j.isci.2020.101914] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/11/2020] [Accepted: 12/03/2020] [Indexed: 11/22/2022] Open
Abstract
Adsorption-driven osmotic heat engines offer an alternative way for harvesting low-grade waste heat below 80°C. In this study, we performed a high-throughput computational screening based on grand canonical Monte Carlo simulations to identify the high-performance metal-organic frameworks (MOFs) from 1322 computationally ready experimental MOF structures for adsorption-driven osmotic heat engines with LiCl-methanol as the working fluid. Structure-property relationship analysis reveals that MOFs exhibiting high energy efficiency possess large working capacity, pore size and surface area, and moderate adsorption enthalpy comparable to the evaporation enthalpy. Furthermore, machine learning is employed to accelerate the computational screening for satisfied MOFs via the structure properties. The optimal structure properties of the MOFs are further identified via the ensemble-based regression model by optimizing the energy efficiency via the genetic algorithm, which shed light on rationally designing and fabricating MOFs for desired heat-to-electricity conversion.
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Affiliation(s)
- Rui Long
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Xiaoxiao Xia
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Yanan Zhao
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Song Li
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Zhichun Liu
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Wei Liu
- School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
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19
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Jablonka K, Ongari D, Moosavi SM, Smit B. Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. Chem Rev 2020; 120:8066-8129. [PMID: 32520531 PMCID: PMC7453404 DOI: 10.1021/acs.chemrev.0c00004] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Indexed: 12/16/2022]
Abstract
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
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Affiliation(s)
- Kevin
Maik Jablonka
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Daniele Ongari
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Seyed Mohamad Moosavi
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
| | - Berend Smit
- Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingénierie Chimiques (ISIC), École Polytechnique Fédérale
de Lausanne (EPFL), Sion, Switzerland
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Evaluation of Metal–Organic Frameworks as Potential Adsorbents for Solar Cooling Applications. APPLIED SYSTEM INNOVATION 2020. [DOI: 10.3390/asi3020026] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The reduction of carbon dioxide emissions has become a need of the day to overcome different environmental issues and challenges. The use of alternative and renewable-based technologies is one of the options to achieve the target of sustainable development through the reduction of these harmful emissions. Among different technologies thermally activated cooling systems are one which can reduce the harmful emissions caused by conventional heating, ventilation, and air conditioning technology. Thermal cooling systems utilize different porous materials and work on a reversible adsorption/desorption cycle. Different advancements have been made for this technology but still a lot of work should be done to replace conventional systems with this newly developed technology. High adsorption capacity and lower input heat are two major requirements for efficient thermally driven cooling technologies. In this regard, it is a need of the day to develop novel adsorbents with high sorption capacity and low regeneration temperature. Due to tunable topologies and a highly porous nature, the hybrid porous crystalline materials known as metal–organic frameworks (MOFs) are a great inspiration for thermally driven adsorption-based cooling applications. Keeping all the above-mentioned aspects in mind, this paper presents a comprehensive overview of the potential use of MOFs as adsorbent material for adsorption and desiccant cooling technologies. A detailed overview of MOFs, their structure, and their stability are presented. This review will be helpful for the research community to have updated research progress in MOFs and their potential use for adsorption-based cooling systems.
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Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10020569] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.
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