Machine-Learning Prediction of Metal-Organic Framework Guest Accessibility from Linker and Metal Chemistry.

Pétuya, Rémi; Durdy, Samantha; Antypov, Dmytro; Gaultois, Michael W; Berry, Neil G; Darling, George R; Katsoulidis, Alexandros P; Dyer, Matthew S; Rosseinsky, Matthew J

Pétuya, Rémi; Durdy, Samantha; Antypov, Dmytro; Gaultois, Michael W; Berry, Neil G; Darling, George R; Katsoulidis, Alexandros P; Dyer, Matthew S; Rosseinsky, Matthew J.

Afiliação

Pétuya R; Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Durdy S; Present address: Nextmol (Bytelab Solutions SL), 08018, Barcelona, Spain.
Antypov D; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Gaultois MW; Department of Computer Science, University of Liverpool, Ashton Street, Liverpool, L69 3BX, UK.
Berry NG; Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Darling GR; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Katsoulidis AP; Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Dyer MS; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.
Rosseinsky MJ; Department of Chemistry and Materials Innovation Factory, University of Liverpool, 51 Oxford Street, Liverpool, L7 3NY, UK.

Angew Chem Int Ed Engl ; 61(9): e202114573, 2022 Feb 21.

Article em En | MEDLINE | ID: mdl-34878706

Palavras-chave

Database; Guest accessibility; Machine learning; Metal-organic frameworks; Porosity

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Angew Chem Int Ed Engl Ano de publicação: 2022 Tipo de documento: Article

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