1
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Daglar H, Gulbalkan HC, Aksu GO, Keskin S. Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2405532. [PMID: 39072794 DOI: 10.1002/adma.202405532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/08/2024] [Indexed: 07/30/2024]
Abstract
Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.
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Affiliation(s)
- Hilal Daglar
- Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey
| | - Hasan Can Gulbalkan
- Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey
| | - Gokhan Onder Aksu
- Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey
| | - Seda Keskin
- Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul, 34450, Turkey
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2
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Korolev V, Mitrofanov A. Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design. J Chem Inf Model 2024; 64:1919-1931. [PMID: 38456446 DOI: 10.1021/acs.jcim.3c02083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.
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Affiliation(s)
- Vadim Korolev
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
| | - Artem Mitrofanov
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow 119192, Russia
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3
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Demir H, Daglar H, Gulbalkan HC, Aksu GO, Keskin S. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning. Coord Chem Rev 2023. [DOI: 10.1016/j.ccr.2023.215112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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4
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Yan T, Bi Z, Liu D, Zhang X, Lu G, Yang Q. A Self-Evolutionary Methodology for Reverse Design of Novel MOFs. J Phys Chem A 2022; 126:8476-8486. [DOI: 10.1021/acs.jpca.2c05647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Tongan Yan
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing100029, China
| | - Zhiyuan Bi
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing100029, China
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing100029, China
| | - Dahuan Liu
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing100029, China
| | - Xiaonan Zhang
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing100029, China
| | - Gang Lu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing100029, China
| | - Qingyuan Yang
- State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing100029, China
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5
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Nath K, Ahmed A, Siegel DJ, Matzger AJ. Computational Identification and Experimental Demonstration of High-Performance Methane Sorbents. Angew Chem Int Ed Engl 2022; 61:e202203575. [PMID: 35478372 PMCID: PMC9322563 DOI: 10.1002/anie.202203575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Indexed: 01/27/2023]
Abstract
Remarkable methane uptake is demonstrated experimentally in three metal-organic frameworks (MOFs) identified by computational screening: UTSA-76, UMCM-152 and DUT-23-Cu. These MOFs outperform the benchmark sorbent, HKUST-1, both volumetrically and gravimetrically, under a pressure swing of 80 to 5 bar at 298 K. Although high uptake at elevated pressure is critical for achieving this performance, a low density of high-affinity sites (coordinatively unsaturated metal centers) also contributes to a more complete release of stored gas at low pressure. The identification of these MOFs facilitates the efficient storage of natural gas via adsorption and provides further evidence of the utility of computational screening in identifying overlooked sorbents.
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Affiliation(s)
- Karabi Nath
- Department of Chemistry and Macromolecular Science and Engineering ProgramUniversity of Michigan930 North University AvenueAnn ArborMI 48109USA
| | - Alauddin Ahmed
- Mechanical Engineering DepartmentUniversity of MichiganAnn ArborMI 48109USA
- Materials Science and EngineeringApplied Physics Program, and University of Michigan Energy InstituteUniversity of MichiganAnn ArborMI 48109USA
| | - Donald J. Siegel
- Mechanical Engineering DepartmentUniversity of MichiganAnn ArborMI 48109USA
- Materials Science and EngineeringApplied Physics Program, and University of Michigan Energy InstituteUniversity of MichiganAnn ArborMI 48109USA
- Current address: Walker Department of Mechanical EngineeringTexas Materials Institute and Oden Institute for Computational Engineering and SciencesUniversity of Texas at Austin204 E. Dean Keeton Street, ETC II 5.160AustinTX 78712-1591USA
| | - Adam J. Matzger
- Department of Chemistry and Macromolecular Science and Engineering ProgramUniversity of Michigan930 North University AvenueAnn ArborMI 48109USA
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6
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Nath K, Ahmed A, Siegel DJ, Matzger AJ. Computational Identification and Experimental Demonstration of High‐Performance Methane Sorbents. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202203575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Karabi Nath
- Department of Chemistry and Macromolecular Science and Engineering Program University of Michigan 930 North University Avenue Ann Arbor MI 48109 USA
| | - Alauddin Ahmed
- Mechanical Engineering Department University of Michigan Ann Arbor MI 48109 USA
- Materials Science and Engineering Applied Physics Program, and University of Michigan Energy Institute University of Michigan Ann Arbor MI 48109 USA
| | - Donald J. Siegel
- Mechanical Engineering Department University of Michigan Ann Arbor MI 48109 USA
- Materials Science and Engineering Applied Physics Program, and University of Michigan Energy Institute University of Michigan Ann Arbor MI 48109 USA
- Current address: Walker Department of Mechanical Engineering Texas Materials Institute and Oden Institute for Computational Engineering and Sciences University of Texas at Austin 204 E. Dean Keeton Street, ETC II 5.160 Austin TX 78712-1591 USA
| | - Adam J. Matzger
- Department of Chemistry and Macromolecular Science and Engineering Program University of Michigan 930 North University Avenue Ann Arbor MI 48109 USA
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7
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Zhang K, Wu J, Yoo H, Lee Y. Machine Learning-based approach for Tailor-Made design of ionic Liquids: Application to CO2 capture. Sep Purif Technol 2021. [DOI: 10.1016/j.seppur.2021.119117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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8
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Beauregard N, Pardakhti M, Srivastava R. In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption. J Chem Inf Model 2021; 61:3232-3239. [PMID: 34264660 DOI: 10.1021/acs.jcim.0c01479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The increased use of transition fuels, such as natural gas, and the resulting increase in methane emissions have resulted in a need for novel methane storage materials. Metal-organic frameworks (MOFs) have shown promise as efficient storage materials. A virtually limitless number of potential MOFs can be hypothesized, which exhibit a wide variety of different structural and chemical characteristics. Because of the numerous possibilities, identification of the best MOF for methane storage can be a potentially challenging problem. In this work, determination of the best such MOF was cast as an inverse function problem. The function, a random forest (RF) model using 12 structural and chemical descriptors, was trained on 10% of a data set consisting of 130 398 hypothetical MOFs (hMOFs) to predict simulated methane uptake. The RF model was tested on the remaining 90% of the data. After validation, a genetic algorithm (GA) was used to evolve in silico the best MOFs for methane adsorption. The RF model was imbedded into the GA as the fitness function to predict the methane uptake of the evolved MOFs (eMOFs). The best 15 eMOFs matched hMOFs found in the top 1% of the database. Nine of the 15 eMOFs were found in the top 0.1%. More impressively, two of the eMOFs matched the top two hypothetical MOFs with the highest methane uptake values out of the entire database of 130 398 MOFs. Further, by leveraging the ensemble nature of the GA, it was possible to characterize the importance of the different material properties for methane adsorption, providing fundamental insight for future material design strategies.
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Affiliation(s)
- Nicole Beauregard
- Department of Chemical and Biomolecular Engineering, University of Connecticut, 191 Auditorium Rd. Unit 3222, Storrs, Connecticut 06269, United States
| | - Maryam Pardakhti
- Department of Chemical and Biomolecular Engineering, University of Connecticut, 191 Auditorium Rd. Unit 3222, Storrs, Connecticut 06269, United States.,Department of Computer Science & Engineering, University of Connecticut, Storrs, Connecticut 06269, United States.,Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ranjan Srivastava
- Department of Chemical and Biomolecular Engineering, University of Connecticut, 191 Auditorium Rd. Unit 3222, Storrs, Connecticut 06269, United States.,Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
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9
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Ahmed A, Siegel DJ. Predicting hydrogen storage in MOFs via machine learning. PATTERNS (NEW YORK, N.Y.) 2021; 2:100291. [PMID: 34286305 PMCID: PMC8276024 DOI: 10.1016/j.patter.2021.100291] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 11/14/2022]
Abstract
The H2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (>5,300 m2 g-1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g-1). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.
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Affiliation(s)
- Alauddin Ahmed
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
| | - Donald J. Siegel
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
- Materials Science & Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
- University of Michigan Energy Institute, University of Michigan, Ann Arbor, MI 48109, USA
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10
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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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11
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Zhang X, Zhang K, Lee Y. Machine Learning Enabled Tailor-Made Design of Application-Specific Metal-Organic Frameworks. ACS APPLIED MATERIALS & INTERFACES 2020; 12:734-743. [PMID: 31820913 DOI: 10.1021/acsami.9b17867] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In the development of advanced nanoporous materials, one clear and unavoidable challenge in hand is the sheer size (in principle, infinite) of the materials space to be explored. While high-throughput screening techniques allow us to narrow down the enormous-scale database of nanoporous materials, there are still practical limitations stemming from a costly molecular simulation in estimating a material's performance and the necessity of a sophisticated descriptor identifying materials. With an attempt to transition away from the screening-based approaches, this paper presents a computational approach combining the Monte Carlo tree search and recurrent neural networks for the tailor-made design of metal-organic frameworks toward the desired target applications. In the demonstration cases for methane-storage and carbon-capture applications, our approach showed significant efficiency in designing promising and novel metal-organic frameworks. We expect that this approach would easily be extended to other applications by simply adjusting the reward function according to the target performance property.
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Affiliation(s)
- Xiangyu Zhang
- School of Physical Science and Technology , ShanghaiTech University , Shanghai 201210 , China
| | - Kexin Zhang
- School of Physical Science and Technology , ShanghaiTech University , Shanghai 201210 , China
| | - Yongjin Lee
- School of Physical Science and Technology , ShanghaiTech University , Shanghai 201210 , China
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12
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Kim B, Lee S, Kim J. Inverse design of porous materials using artificial neural networks. SCIENCE ADVANCES 2020; 6:eaax9324. [PMID: 31922005 PMCID: PMC6941911 DOI: 10.1126/sciadv.aax9324] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 11/07/2019] [Indexed: 05/19/2023]
Abstract
Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions, and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design of porous materials.
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13
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Xu H, Luo X, Wang J, Su Y, Zhao X, Li Y. Spherical Sandwich Au@Pd@UIO-67/Pt@UIO- n ( n = 66, 67, 69) Core-Shell Catalysts: Zr-Based Metal-Organic Frameworks for Effectively Regulating the Reverse Water-Gas Shift Reaction. ACS APPLIED MATERIALS & INTERFACES 2019; 11:20291-20297. [PMID: 31070880 DOI: 10.1021/acsami.9b04748] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this study, spherical sandwich Au@Pd@UIO-67/Pt@UIO- n ( n = 66, 67, 69) core-shell catalysts were assembled. Au nanoparticles (NPs) were used as the core for the epitaxial growth of Pd shells, and Au@Pd core-shell NPs were successfully encapsulated in the center of monodispersed Au@Pd@UIO-67 nanospheres. Pt NPs were fully fixed onto the nanosphere surfaces to obtain Au@Pd@UIO-67/Pt composites; further coating with UIO- n led to Au@Pd@UIO-67/Pt@UIO- n, in which Pt NPs are sandwiched between the Au@Pd@UIO-67 core and the UIO- n shell. The Au@Pd core-shell NPs efficiently controlled the morphology and structure of UIO-67 and enhanced the CO selectivity of the catalyst. Pt NPs increased the CO2 conversion, and the UIO- n component effectively regulated the reverse water-gas shift reaction.
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Affiliation(s)
- Haitao Xu
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
| | - Xikuo Luo
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
| | - Jiajia Wang
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
| | - Yuqun Su
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
| | - Xi Zhao
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
| | - Yansong Li
- State Key Laboratory of Chemical Engineering, Membrane Science and Engineering R&D Lab, Chemical Engineering Re-search Center , East China University of Science and Technology (ECUST) , 130 Meilong Road , Shanghai 200237 , China
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14
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Mace A, Barthel S, Smit B. Automated Multiscale Approach To Predict Self-Diffusion from a Potential Energy Field. J Chem Theory Comput 2019; 15:2127-2141. [PMID: 30811190 PMCID: PMC6460401 DOI: 10.1021/acs.jctc.8b01255] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
For large-scale screening studies
there is a need to estimate the
diffusion of gas molecules in nanoporous materials more efficiently
than (brute force) molecular dynamics. In particular for systems with
low diffusion coefficients molecular dynamics can be prohibitively
expensive. An alternative is to compute the hopping rates between
adsorption sites using transition state theory. For large-scale screening
this requires the automatic detection of the transition states between
the adsorption sites along the different diffusion paths. Here an
algorithm is presented that analyzes energy grids for the moving particles.
It detects the energies at which diffusion paths are formed, together
with their directions. This allows for easy identification of nondiffusive
systems. For diffusive systems, it partitions the grid coordinates
assigned to energy basins and transitions states, permitting a transition
state theory based analysis of the diffusion. We test our method on
CH4 diffusion in zeolites, using a standard kinetic Monte
Carlo simulation based on the output of our grid analysis. We find
that it is accurate, fast, and rigorous without limitations to the
geometries of the diffusion tunnels or transition states.
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Affiliation(s)
- Amber Mace
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais , Ecole Polytechnique Fédérale de Lausanne (EPFL) , Rue de l'Industrie 17 , CH-1951 Sion , Switzerland.,Department of Materials and Environmental Chemistry , Stockholm University , SE-106 91 Stockholm , Sweden
| | - Senja Barthel
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais , Ecole Polytechnique Fédérale de Lausanne (EPFL) , Rue de l'Industrie 17 , CH-1951 Sion , Switzerland
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais , Ecole Polytechnique Fédérale de Lausanne (EPFL) , Rue de l'Industrie 17 , CH-1951 Sion , Switzerland
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15
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Ahmed A, Seth S, Purewal J, Wong-Foy AG, Veenstra M, Matzger AJ, Siegel DJ. Exceptional hydrogen storage achieved by screening nearly half a million metal-organic frameworks. Nat Commun 2019; 10:1568. [PMID: 30952862 PMCID: PMC6450936 DOI: 10.1038/s41467-019-09365-w] [Citation(s) in RCA: 127] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 03/08/2019] [Indexed: 12/03/2022] Open
Abstract
Few hydrogen adsorbents balance high usable volumetric and gravimetric capacities. Although metal-organic frameworks (MOFs) have recently demonstrated progress in closing this gap, the large number of MOFs has hindered the identification of optimal materials. Here, a systematic assessment of published databases of real and hypothetical MOFs is presented. Nearly 500,000 compounds were screened computationally, and the most promising were assessed experimentally. Three MOFs with capacities surpassing that of IRMOF-20, the record-holder for balanced hydrogen capacity, are demonstrated: SNU-70, UMCM-9, and PCN-610/NU-100. Analysis of trends reveals the existence of a volumetric ceiling at ∼40 g H2 L-1. Surpassing this ceiling is proposed as a new capacity target for hydrogen adsorbents. Counter to earlier studies of total hydrogen uptake in MOFs, usable capacities in the highest-capacity materials are negatively correlated with density and volumetric surface area. Instead, capacity is maximized by increasing gravimetric surface area and porosity. This suggests that property/performance trends for total capacities may not translate to usable capacities.
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Affiliation(s)
- Alauddin Ahmed
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Saona Seth
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Justin Purewal
- Ford Motor Company, Research and Advanced Engineering, 1201 Village Rd., Dearborn, MI, 48121, United States
| | - Antek G Wong-Foy
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Mike Veenstra
- Ford Motor Company, Research and Advanced Engineering, 1201 Village Rd., Dearborn, MI, 48121, United States
| | - Adam J Matzger
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Donald J Siegel
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI, 48109, United States.
- Materials Science & Engineering, University of Michigan, Ann Arbor, MI, 48109, United States.
- Applied Physics Program, University of Michigan, Ann Arbor, MI, 48109, United States.
- University of Michigan Energy Institute, University of Michigan, Ann Arbor, MI, 48109, United States.
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16
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Tang D, Wu Y, Verploegh RJ, Sholl DS. Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules. CHEMSUSCHEM 2018; 11:1567-1575. [PMID: 29624911 DOI: 10.1002/cssc.201702289] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 03/13/2018] [Accepted: 04/01/2018] [Indexed: 06/08/2023]
Abstract
Although computational models have been used to predict adsorption of molecules in large libraries of porous adsorbents, previous work of this kind has focused on a small number of molecules as potential adsorbates. In this study, molecular simulations were used to consider the adsorption of a diverse range of molecules in a large collection of metal-organic framework (MOF) materials. Specifically, 11 304 isotherms were obtained from molecular simulations of 24 different adsorbates in 471 MOFs. This information provides insight into several interesting questions that could not be addressed with previously available data. Highly computationally efficient methods are introduced that can predict isotherms for a wide range of adsorbing molecules with far less computation than traditional molecular simulations. By characterizing the 276 binary mixtures defined by the molecules considered, "privileged" adsorbents are shown to exist, which are effective for separating many different molecular mixtures. Finally, correlations that were developed previously to predict molecular solubility in polymers are found to be surprisingly effective in predicting the average properties of molecules adsorbing in MOFs.
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Affiliation(s)
- Dai Tang
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, Georgia, 30332-0100, USA
| | - Ying Wu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, Georgia, 30332-0100, USA
- School of Chemical and Chemical Engineering, South China University of Technology, Guangzhou, China
| | - Ross J Verploegh
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, Georgia, 30332-0100, USA
| | - David S Sholl
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, Georgia, 30332-0100, USA
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17
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Thornton AW, Simon CM, Kim J, Kwon O, Deeg KS, Konstas K, Pas SJ, Hill MR, Winkler DA, Haranczyk M, Smit B. Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2017; 29:2844-2854. [PMID: 28413259 PMCID: PMC5390509 DOI: 10.1021/acs.chemmater.6b04933] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/06/2017] [Indexed: 05/29/2023]
Abstract
The Materials Genome is in action: the molecular codes for millions of materials have been sequenced, predictive models have been developed, and now the challenge of hydrogen storage is targeted. Renewably generated hydrogen is an attractive transportation fuel with zero carbon emissions, but its storage remains a significant challenge. Nanoporous adsorbents have shown promising physical adsorption of hydrogen approaching targeted capacities, but the scope of studies has remained limited. Here the Nanoporous Materials Genome, containing over 850 000 materials, is analyzed with a variety of computational tools to explore the limits of hydrogen storage. Optimal features that maximize net capacity at room temperature include pore sizes of around 6 Å and void fractions of 0.1, while at cryogenic temperatures pore sizes of 10 Å and void fractions of 0.5 are optimal. Our top candidates are found to be commercially attractive as "cryo-adsorbents", with promising storage capacities at 77 K and 100 bar with 30% enhancement to 40 g/L, a promising alternative to liquefaction at 20 K and compression at 700 bar.
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Affiliation(s)
- Aaron W. Thornton
- Future Industries, Commonwealth Scientific and Industrial Research Organisation, Private Bag 10, Clayton Soutth MDC, Victoria 3169, Australia
| | - Cory M. Simon
- Department of Chemical and Biomolecular Engineering and Department of Chemistry, University of California, Berkeley, California 94720-1462, United States
| | - Jihan Kim
- Department
of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong-gu, Daejeon, 305-701, Korea
| | - Ohmin Kwon
- Department
of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong-gu, Daejeon, 305-701, Korea
| | - Kathryn S. Deeg
- Department of Chemical and Biomolecular Engineering and Department of Chemistry, University of California, Berkeley, California 94720-1462, United States
| | - Kristina Konstas
- Future Industries, Commonwealth Scientific and Industrial Research Organisation, Private Bag 10, Clayton Soutth MDC, Victoria 3169, Australia
| | - Steven J. Pas
- Power & Energy Systems, Maritime Division, Defence Science and
Technology Group, Department of Defence, 506 Lorimer Street, Fishermans Bend, Victoria 3207, Australia
- School of Chemistry and Department of Chemical
Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - Matthew R. Hill
- Future Industries, Commonwealth Scientific and Industrial Research Organisation, Private Bag 10, Clayton Soutth MDC, Victoria 3169, Australia
- School of Chemistry and Department of Chemical
Engineering, Monash University, Clayton, Victoria 3800, Australia
| | - David A. Winkler
- Future Industries, Commonwealth Scientific and Industrial Research Organisation, Private Bag 10, Clayton Soutth MDC, Victoria 3169, Australia
- Monash
Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville, Victoria 3052, Australia
- Latrobe Institute for Molecular Science, Bundoora, Victoria 3046, Australia
- School of Chemical
and Physical Sciences, Flinders University, Bedford Park, South Australia 5042, Australia
| | - Maciej Haranczyk
- Computational
Research Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720-8139, United States
| | - Berend Smit
- Department of Chemical and Biomolecular Engineering and Department of Chemistry, University of California, Berkeley, California 94720-1462, United States
- Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, Valais, Rue de l’Industrie
17, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1950 Sion, Switzerland
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A rare twofold interpenetrating NbO mixed-ligand mesomeric network from two individual heterochiral 3D frameworks. INORG CHEM COMMUN 2016. [DOI: 10.1016/j.inoche.2016.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Collins SP, Daff TD, Piotrkowski SS, Woo TK. Materials design by evolutionary optimization of functional groups in metal-organic frameworks. SCIENCE ADVANCES 2016; 2:e1600954. [PMID: 28138523 PMCID: PMC5262444 DOI: 10.1126/sciadv.1600954] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 10/20/2016] [Indexed: 05/25/2023]
Abstract
A genetic algorithm that efficiently optimizes a desired physical or functional property in metal-organic frameworks (MOFs) by evolving the functional groups within the pores has been developed. The approach has been used to optimize the CO2 uptake capacity of 141 experimentally characterized MOFs under conditions relevant for postcombustion CO2 capture. A total search space of 1.65 trillion structures was screened, and 1035 derivatives of 23 different parent MOFs were identified as having exceptional CO2 uptakes of >3.0 mmol/g (at 0.15 atm and 298 K). Many well-known MOF platforms were optimized, with some, such as MIL-47, having their CO2 adsorption increase by more than 400%. The structures of the high-performing MOFs are provided as potential targets for synthesis.
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Chung YG, Gómez-Gualdrón DA, Li P, Leperi KT, Deria P, Zhang H, Vermeulen NA, Stoddart JF, You F, Hupp JT, Farha OK, Snurr RQ. In silico discovery of metal-organic frameworks for precombustion CO 2 capture using a genetic algorithm. SCIENCE ADVANCES 2016; 2:e1600909. [PMID: 27757420 PMCID: PMC5065252 DOI: 10.1126/sciadv.1600909] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 09/01/2016] [Indexed: 05/20/2023]
Abstract
Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here.
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Affiliation(s)
- Yongchul G. Chung
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Diego A. Gómez-Gualdrón
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO 80401, USA
| | - Peng Li
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Karson T. Leperi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Pravas Deria
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Hongda Zhang
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | | | - J. Fraser Stoddart
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Fengqi You
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Joseph T. Hupp
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Omar K. Farha
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
- Department of Chemistry, Faculty of Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Randall Q. Snurr
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
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Yin D, Huang G, Sun Q, Li Q, Wang X, Yuan D, Wang C, Wang L. RGO/Co 3 O 4 Composites Prepared Using GO-MOFs as Precursor for Advanced Lithium-ion Batteries and Supercapacitors Electrodes. Electrochim Acta 2016. [DOI: 10.1016/j.electacta.2016.08.110] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Paik D, Haranczyk M, Kim J. Towards accurate porosity descriptors based on guest-host interactions. J Mol Graph Model 2016; 66:91-8. [PMID: 27054971 DOI: 10.1016/j.jmgm.2016.03.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 03/23/2016] [Accepted: 03/23/2016] [Indexed: 10/22/2022]
Abstract
For nanoporous materials at the characterization level, geometry-based approaches have become the methods of choice to provide information, often encoded in numerical descriptors, about the pores and the channels of a porous material. Examples of most common descriptors of the latter are pore limiting diameters, accessible surface area and accessible volume. The geometry-based methods exploit hard-sphere approximation for atoms, which (1) reduces costly computations of the interatomic interactions between the probe guest molecule and the porous material framework atoms, (2) effectively exploit applied mathematics methods such as Voronoi decomposition to represent and characterize porosity. In this work, we revisit and quantify the shortcoming of the geometry-based approaches. To do so, we have developed a series of algorithms to calculate pore descriptors such as void fraction, accessible surface area, pore limiting diameters (largest included sphere, and largest free sphere) based on a classical force field model of interactions between the guest and the framework atoms. Our resulting energy-based methods are tested on diverse sets of metal-organic frameworks and zeolite structures and comparisons against results obtained from geometric-based method indicate deviations in the cases for structures with small pore sizes. The method provides both high accuracy and performance making it suitable when screening a large database of materials.
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Affiliation(s)
- Dooam Paik
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
| | - Maciej Haranczyk
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, United States; IMDEA Materials Institute, Calle Eric Kandel, 2, 28906 Getafe, Madrid, Spain.
| | - Jihan Kim
- Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
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