TBMaLT, a flexible toolkit for combining tight-binding and machine learning.

McSloy, A; Fan, G; Sun, W; Hölzer, C; Friede, M; Ehlert, S; Schütte, N-E; Grimme, S; Frauenheim, T; Aradi, B

McSloy, A; Fan, G; Sun, W; Hölzer, C; Friede, M; Ehlert, S; Schütte, N-E; Grimme, S; Frauenheim, T; Aradi, B.

Afiliação

McSloy A; Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom.
Fan G; Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany.
Sun W; Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany.
Hölzer C; Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany.
Friede M; Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany.
Ehlert S; Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany.
Schütte NE; Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany.
Grimme S; Mulliken Center for Theoretical Chemistry, University of Bonn, 53115 Bonn, Germany.
Frauenheim T; Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany.
Aradi B; Bremen Center of Computational Materials Science, University of Bremen, 28359 Bremen, Germany.

J Chem Phys ; 158(3): 034801, 2023 Jan 21.

Article em En | MEDLINE | ID: mdl-36681630

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Aprendizado de Máquina

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina Tipo de estudo: Prognostic_studies Idioma: En Revista: J Chem Phys Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

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