Accurate molecular polarizabilities with coupled cluster theory and machine learning.

Wilkins, David M; Grisafi, Andrea; Yang, Yang; Lao, Ka Un; DiStasio, Robert A; Ceriotti, Michele

Wilkins, David M; Grisafi, Andrea; Yang, Yang; Lao, Ka Un; DiStasio, Robert A; Ceriotti, Michele.

Afiliação

Wilkins DM; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Grisafi A; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
Yang Y; Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853.
Lao KU; Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853.
DiStasio RA; Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853 distasio@cornell.edu michele.ceriotti@epfl.ch.
Ceriotti M; Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; distasio@cornell.edu michele.ceriotti@epfl.ch.

Proc Natl Acad Sci U S A ; 116(9): 3401-3406, 2019 02 26.

Article em En | MEDLINE | ID: mdl-30733292

Palavras-chave

Gaussian process regression; coupled cluster theory; density functional theory; dipole polarizability; machine learning

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Suíça

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google