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Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host–Guest Interactions in Metal- and Covalent Organic Frameworks
[Image: see text] In recent years, research focused on synthesis, characterization, and application of metal–organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB)...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331828/ https://www.ncbi.nlm.nih.gov/pubmed/37352552 http://dx.doi.org/10.1021/acs.jpclett.3c00941 |
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author | Hofer, Thomas S. Listyarini, Risnita Vicky Hajdarevic, Emir Maier, Lukas Purtscher, Felix R. S. Gamper, Jakob Hanser, Friedrich |
author_facet | Hofer, Thomas S. Listyarini, Risnita Vicky Hajdarevic, Emir Maier, Lukas Purtscher, Felix R. S. Gamper, Jakob Hanser, Friedrich |
author_sort | Hofer, Thomas S. |
collection | PubMed |
description | [Image: see text] In recent years, research focused on synthesis, characterization, and application of metal–organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB) in conjunction with a suitable constrained molecular dynamics (MD) simulation protocol provides a versatile and flexible platform for the study of pristine MOFs as well as guest@MOF systems. Although being a semi-empirical quantum mechanical method, SCC DFTB inherently accounts for polarization and many-body contributions, which may become a limiting factor in purely force field-based simulation studies. A number of examples such as CO(2), indigo, and drug molecules embedded in various MOF hosts are discussed to highlight the capabilities of the presented simulation approach. Furthermore, a promising extension of the outlined simulation strategy toward the treatment of covalent organic frameworks utilizing state-of-the-art neural network potentials providing a description at DFT accuracy and force field cost is outlined. |
format | Online Article Text |
id | pubmed-10331828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-103318282023-07-11 Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host–Guest Interactions in Metal- and Covalent Organic Frameworks Hofer, Thomas S. Listyarini, Risnita Vicky Hajdarevic, Emir Maier, Lukas Purtscher, Felix R. S. Gamper, Jakob Hanser, Friedrich J Phys Chem Lett [Image: see text] In recent years, research focused on synthesis, characterization, and application of metal–organic frameworks (MOFs) has attracted increased interest, from both an experimental as well as a theoretical perspective. Self-consistent charge density functional tight binding (SCC DFTB) in conjunction with a suitable constrained molecular dynamics (MD) simulation protocol provides a versatile and flexible platform for the study of pristine MOFs as well as guest@MOF systems. Although being a semi-empirical quantum mechanical method, SCC DFTB inherently accounts for polarization and many-body contributions, which may become a limiting factor in purely force field-based simulation studies. A number of examples such as CO(2), indigo, and drug molecules embedded in various MOF hosts are discussed to highlight the capabilities of the presented simulation approach. Furthermore, a promising extension of the outlined simulation strategy toward the treatment of covalent organic frameworks utilizing state-of-the-art neural network potentials providing a description at DFT accuracy and force field cost is outlined. American Chemical Society 2023-06-23 /pmc/articles/PMC10331828/ /pubmed/37352552 http://dx.doi.org/10.1021/acs.jpclett.3c00941 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Hofer, Thomas S. Listyarini, Risnita Vicky Hajdarevic, Emir Maier, Lukas Purtscher, Felix R. S. Gamper, Jakob Hanser, Friedrich Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host–Guest Interactions in Metal- and Covalent Organic Frameworks |
title | Beyond the Status Quo: Density Functional Tight Binding
and Neural Network Potentials as a Versatile Simulation Strategy to
Characterize Host–Guest Interactions in Metal- and Covalent
Organic Frameworks |
title_full | Beyond the Status Quo: Density Functional Tight Binding
and Neural Network Potentials as a Versatile Simulation Strategy to
Characterize Host–Guest Interactions in Metal- and Covalent
Organic Frameworks |
title_fullStr | Beyond the Status Quo: Density Functional Tight Binding
and Neural Network Potentials as a Versatile Simulation Strategy to
Characterize Host–Guest Interactions in Metal- and Covalent
Organic Frameworks |
title_full_unstemmed | Beyond the Status Quo: Density Functional Tight Binding
and Neural Network Potentials as a Versatile Simulation Strategy to
Characterize Host–Guest Interactions in Metal- and Covalent
Organic Frameworks |
title_short | Beyond the Status Quo: Density Functional Tight Binding
and Neural Network Potentials as a Versatile Simulation Strategy to
Characterize Host–Guest Interactions in Metal- and Covalent
Organic Frameworks |
title_sort | beyond the status quo: density functional tight binding
and neural network potentials as a versatile simulation strategy to
characterize host–guest interactions in metal- and covalent
organic frameworks |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331828/ https://www.ncbi.nlm.nih.gov/pubmed/37352552 http://dx.doi.org/10.1021/acs.jpclett.3c00941 |
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