From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer

Li, Aurelia; Bueno-Pérez, Rocío; Madden, David G.; Fairen‐Jimenez, David

doi:10.1039/d2sc01254e

Cited by 10 publications

(9 citation statements)

References 105 publications

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“…Among the materials that are determined to be synthesizable, those with low synthesis cost would be preferred in practical applications, thus MOF cost should be one of the selection criteria in addition to performance. [156] To sum up, performing force field-based molecular simulations, DFT simulations, and establishing ML models can provide multiple benefits for advancing the understanding of MOF-based membrane structures and the underlying reasons behind their separation performances. The force field-based molecular simulations enable efficient sampling of the porous space in MOFbased membranes for gas adsorption and diffusion.…”

Section: Discussionmentioning

confidence: 99%

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A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science

Demir,

Keskin

2023

Macro Materials & Eng

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Membrane‐based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks‐ (MOFs) are considered as next‐generation membrane materials that can bring about high separation performance and energy efficiency in various separation applications. Yet, the enormously large number of possible MOF structures necessitates the development and implementation of efficient modeling approaches to expedite the design, discovery, and selection of optimal MOF‐based membranes via directing the experimental efforts, time, and resources to the potentially useful membrane materials. With the recent developments in the field of atomic simulations and artificial intelligence methods, a new era of membrane modeling has started. This review focuses on the recent advances made and key strategies used in the modeling of MOF‐based membranes and highlight the huge potential of combining atomistic modeling of MOFs with machine learning to explore very large number of MOF membranes and MOF/polymer composite membranes for gas separation. Opportunities and challenges related to the implementation of data‐driven approaches to extract useful structure–property relations of MOF‐based membranes and to produce design principles for the high‐performing MOF‐based membranes are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science

Demir,

Keskin

2023

Macro Materials & Eng

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“…Hence, computer simulations provide excellent tools to further study and screen these materials. [ 96 ] Computer simulations allow the achievement of a deep molecular‐level understanding of the mechanisms governing host‐guest interactions, the testing of possible structural and/or chemical changes in the frameworks and the development of models based on experimental data, that will enable studies under conditions not possible experimentally. However, there may also be some drawbacks: computational approaches and models may not be accurate enough to represent the system being studied; some experimental samples may be difficult to properly describe computationally, namely due to defects or impurities, not always obvious to implement on the perfect structures of MOFs.…”

Section: Computational Studies Of Materialsmentioning

confidence: 99%

Multiscale Computational Approaches toward the Understanding of Materials

Bordonhos

Galvão

Gomes

et al. 2022

Advcd Theory and Sims

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Herewith, an overview of the group's collaborative research efforts on the development and deployment of computational approaches to understand materials and tools at different length and time scales is presented. The techniques employed range from quantum mechanical approaches based on the density functional theory to classical atomistic and coarse-grained force field methods, targeting molecular systems composed of a few to several million atoms at different levels of detail. These new tools and molecular models are presented to the computational materials science community so they can be used in more realistic molecular modelling studies of the properties of materials and their dependence on subtle modifications of their structures. The review concludes by presenting a selection of recent computational case-studies oriented toward the understanding of the synthesis of materials, the interpretation of unexpected experimental results, the prediction of material properties, and the materials selection based on their characteristics for applications in areas such as gas adsorption/separation, corrosion protection, and catalysis.

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“…High throughput computational screening (HTCS) has emerged as an important strategy in the efforts to identify the most promising metal organic frameworks (MOFs) for carbon capture applications. 1,2 Consider two academic groups as an example: both are seeking to determine the best MOF for carbon capture from flue gas using computational screening. Their simulation protocols are identical, including the database of MOF candidates.…”

Section: Introductionmentioning

confidence: 99%

A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture

Cleeton

Oliveira

Neumann

et al. 2023

Energy Environ. Sci.

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From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer

Abstract: Metal–organic frameworks (MOFs) are one of the most researched designer materials today, as their high tunability offers scientists a wide space to imagine all kinds of possible structures. Their uniquely...

Cited by 10 publications

References 105 publications

A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science

A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science

Multiscale Computational Approaches toward the Understanding of Materials

A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture

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