Coarse-grained fully atomistic machine learning for zeolitic imidazolate frameworks

Coarse-grained 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 chemic...

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Veröffentlicht in:Chemical communications (Cambridge, England) England), 2023-09, Vol.59 (76), p.1145-1148
Hauptverfasser: Faure Beaulieu, Zoé, Nicholas, Thomas C, Gardner, John L. A, Goodwin, Andrew L, Deringer, Volker L
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container_title Chemical communications (Cambridge, England)
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creator Faure Beaulieu, Zoé
Nicholas, Thomas C
Gardner, John L. A
Goodwin, Andrew L
Deringer, Volker L
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. We use atomistic and coarse-grained machine-learning models to address a long-standing question: to what extent are ZIFs analogous to inorganic AB 2 phases?
doi_str_mv 10.1039/d3cc02265j
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title Coarse-grained fully atomistic machine learning for zeolitic imidazolate frameworks
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