Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/140523
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Type: | Journal article |
Title: | Machine learning interatomic potentials for amorphous zeolitic imidazolate frameworks |
Author: | Castel, N. André, D. Edwards, C. Evans, J.D. Coudert, F.-X. |
Citation: | Digital Discovery, 2024; 3(2):355-368 |
Publisher: | Royal Society of Chemistry (RSC) |
Issue Date: | 2024 |
ISSN: | 2635-098X |
Statement of Responsibility: | Nicolas Castel, Dune Andre, Connor Edwards, Jack D. Evans and François-Xavier Couder |
Abstract: | The detailed understanding of the microscopic structure of amorphous phases of metal–organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experimental and computational point of view. In molecular simulations, approaches have been proposed that rely either on reactive force field, that lack chemical accuracy, or first-principles calculations, that are too computationally expensive. Here, we have found an innovative solution to these problems by training a machine learning potential for the description of disordered phases of a zeolitic imidazolate framework (ZIF). We then used it to produce high-quality atomistic models of ZIF glasses, with accuracy close to density functional theory (DFT) but at far lower computational cost in production runs. |
Keywords: | microscopic structure amorphous phases of metal–organic frameworks (MOFs) |
Rights: | © 2024 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. |
DOI: | 10.1039/d3dd00236e |
Grant ID: | http://purl.org/au-research/grants/arc/DE220100163 |
Published version: | http://dx.doi.org/10.1039/d3dd00236e |
Appears in Collections: | Research Outputs |
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hdl_140523.pdf | Published version | 1.13 MB | Adobe PDF | View/Open |
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