Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks

Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB(2) phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemi...

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Detalles Bibliográficos
Autores principales: Faure Beaulieu, Zoé, Nicholas, Thomas C., Gardner, John L. A., Goodwin, Andrew L., Deringer, Volker L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513772/
https://www.ncbi.nlm.nih.gov/pubmed/37668310
http://dx.doi.org/10.1039/d3cc02265j
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author Faure Beaulieu, Zoé
Nicholas, Thomas C.
Gardner, John L. A.
Goodwin, Andrew L.
Deringer, Volker L.
author_facet Faure Beaulieu, Zoé
Nicholas, Thomas C.
Gardner, John L. A.
Goodwin, Andrew L.
Deringer, Volker L.
author_sort Faure Beaulieu, Zoé
collection PubMed
description Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB(2) phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be “coarse-grained” in hybrid framework materials.
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spelling pubmed-105137722023-09-22 Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks Faure Beaulieu, Zoé Nicholas, Thomas C. Gardner, John L. A. Goodwin, Andrew L. Deringer, Volker L. Chem Commun (Camb) Chemistry Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB(2) phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be “coarse-grained” in hybrid framework materials. The Royal Society of Chemistry 2023-08-22 /pmc/articles/PMC10513772/ /pubmed/37668310 http://dx.doi.org/10.1039/d3cc02265j Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Faure Beaulieu, Zoé
Nicholas, Thomas C.
Gardner, John L. A.
Goodwin, Andrew L.
Deringer, Volker L.
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title_full Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title_fullStr Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title_full_unstemmed Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title_short Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
title_sort coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513772/
https://www.ncbi.nlm.nih.gov/pubmed/37668310
http://dx.doi.org/10.1039/d3cc02265j
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