Choosing the right molecular machine learning potential.

Pinheiro, Max; Ge, Fuchun; Ferré, Nicolas; Dral, Pavlo O; Barbatti, Mario

Pinheiro, Max; Ge, Fuchun; Ferré, Nicolas; Dral, Pavlo O; Barbatti, Mario.

Afiliación

Pinheiro M; Aix Marseille University, CNRS, ICR Marseille France max.pinheiro-jr@univ-amu.fr mario.barbatti@univ-amu.fr.
Ge F; State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University China dral@xmu.edu.cn.
Ferré N; Aix Marseille University, CNRS, ICR Marseille France max.pinheiro-jr@univ-amu.fr mario.barbatti@univ-amu.fr.
Dral PO; State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University China dral@xmu.edu.cn.
Barbatti M; Aix Marseille University, CNRS, ICR Marseille France max.pinheiro-jr@univ-amu.fr mario.barbatti@univ-amu.fr.

Chem Sci ; 12(43): 14396-14413, 2021 Nov 10.

Article en En | MEDLINE | ID: mdl-34880991

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Chem Sci Año: 2021 Tipo del documento: Article Pais de publicación: Reino Unido

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