Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Krishnapriyan, Aditi S; Montoya, Joseph; Haranczyk, Maciej; Hummelshøj, Jens; Morozov, Dmitriy

Krishnapriyan, Aditi S; Montoya, Joseph; Haranczyk, Maciej; Hummelshøj, Jens; Morozov, Dmitriy.

Afiliação

Krishnapriyan AS; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. akrishnapriyan@lbl.gov.
Montoya J; Toyota Research Institute, Los Altos, CA, 94022, USA. akrishnapriyan@lbl.gov.
Haranczyk M; Toyota Research Institute, Los Altos, CA, 94022, USA.
Hummelshøj J; IMDEA Materials Institute, C/Eric Kandel 2, 28906, Getafe, Madrid, Spain.
Morozov D; Toyota Research Institute, Los Altos, CA, 94022, USA.

Sci Rep ; 11(1): 8888, 2021 04 26.

Article em En | MEDLINE | ID: mdl-33903606

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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: Sci Rep Ano de publicação: 2021 Tipo de documento: Article

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