scholarly journals The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Melanie T. Huynh ◽  
Alec Kaija ◽  
Caleb Laird ◽  
Sunghyun Yoon ◽  
...  
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Chemical Warfare Agent ◽  
Nanoporous Materials ◽  
Oxygen Storage ◽  
Metal Organic ◽  
Computational Materials ◽  
Computational Discovery

Metal-organic frameworks (MOFs) are highly tunable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have lucidly impacted the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed for molecular simulations, are a platform for computational materials discovery. We pontificate how to orient research efforts to routinize the computational discovery of MOFs for adsorption-based engineering applications.

scholarly journals The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Melanie T. Huynh ◽  
Alec Kaija ◽  
Caleb Laird ◽  
Sunghyun Yoon ◽  
...  
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Chemical Warfare Agent ◽  
Nanoporous Materials ◽  
Oxygen Storage ◽  
Metal Organic ◽  
Computational Materials ◽  
Computational Discovery

Metal-organic frameworks (MOFs) are highly tunable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinize the computational discovery of MOFs for adsorption-based engineering applications.

scholarly journals The Role of Molecular Modeling & Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Melanie T. Huynh ◽  
Alec Kaija ◽  
Caleb Laird ◽  
Sunghyun Yoon ◽  
...  
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Chemical Warfare Agent ◽  
Nanoporous Materials ◽  
Oxygen Storage ◽  
Metal Organic ◽  
Computational Materials ◽  
Computational Discovery

Metal-organic frameworks (MOFs) are highly tunable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinize the computational discovery of MOFs for adsorption-based engineering applications.

Critical role of water stability in metal–organic frameworks and advanced modification strategies for the extension of their applicability

2020 ◽  
Vol 7 (5) ◽  
pp. 1319-1347 ◽  
Author(s):  
Botao Liu ◽  
Kumar Vikrant ◽  
Ki-Hyun Kim ◽  
Vanish Kumar ◽  
Suresh Kumar Kailasa
Keyword(s):  
Drug Delivery ◽  
Water Purification ◽  
Gas Adsorption ◽  
Critical Role ◽  
Metal Organic Frameworks ◽  
Water Stability ◽  
Metal Organic

Metal–organic frameworks (MOFs) are well known for their versatile applications in diverse fields (e.g., gas adsorption, water purification, sensing, drug delivery, and catalysis).

scholarly journals The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

2019 ◽  
Vol 45 (14-15) ◽  
pp. 1082-1121 ◽  
Author(s):  
Arni Sturluson ◽  
Melanie T. Huynh ◽  
Alec R. Kaija ◽  
Caleb Laird ◽  
Sunghyun Yoon ◽  
...  
Keyword(s):  
Molecular Modelling ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Modelling And Simulation ◽  
Metal Organic

scholarly journals Gas adsorption in the topologically disordered Fe-BTC framework

2021 ◽  
Author(s):  
Adam Sapnik ◽  
Christopher W. Ashling ◽  
Lauren Macreadie ◽  
Seok J. Lee ◽  
Timothy Johnson ◽  
...  
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Functional Materials ◽  
Gas Storage ◽  
Metal Organic

Disordered metal–organic frameworks are emerging as an attractive class of functional materials, however their applications in gas storage and separation have yet to be fully explored. Here, we investigate gas...

scholarly journals Predicting the Features of Methane Adsorption in Large Pore Metal-Organic Frameworks for Energy Storage

Nanomaterials ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 818 ◽  
Author(s):  
George Manos ◽  
Lawrence Dunne
Keyword(s):  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Gas Storage ◽  
Methane Adsorption ◽  
Energy Applications ◽  
Adsorbed Gas ◽  
Theoretical Approaches ◽  
Large Pore ◽  
Metal Organic ◽  
Computer Simulation Studies

Currently, metal-organic frameworks (MOFs) are receiving significant attention as part of an international push to use their special properties in an extensive variety of energy applications. In particular, MOFs have exceptional potential for gas storage especially for methane and hydrogen for automobiles. However, using theoretical approaches to investigate this important problem presents various difficulties. Here we present the outcomes of a basic theoretical investigation into methane adsorption in large pore MOFs with the aim of capturing the unique features of this phenomenon. We have developed a pseudo one-dimensional statistical mechanical theory of adsorption of gas in a MOF with both narrow and large pores, which is solved exactly using a transfer matrix technique in the Osmotic Ensemble (OE). The theory effectively describes the distinctive features of adsorption of gas isotherms in MOFs. The characteristic forms of adsorption isotherms in MOFs reflect changes in structure caused by adsorption of gas and compressive stress. Of extraordinary importance for gas storage for energy applications, we find two regimes of Negative gas adsorption (NGA) where gas pressure causes the MOF to transform from the large pore to the narrow pore structure. These transformations can be induced by mechanical compression and conceivably used in an engine to discharge adsorbed gas from the MOF. The elements which govern NGA in MOFs with large pores are identified. Our study may help guide the difficult program of work for computer simulation studies of gas storage in MOFs with large pores.

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

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