Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.

Garrison, Aaron G; Heras-Domingo, Javier; Kitchin, John R; Dos Passos Gomes, Gabriel; Ulissi, Zachary W; Blau, Samuel M

Garrison, Aaron G; Heras-Domingo, Javier; Kitchin, John R; Dos Passos Gomes, Gabriel; Ulissi, Zachary W; Blau, Samuel M.

Afiliación

Garrison AG; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Heras-Domingo J; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Kitchin JR; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Dos Passos Gomes G; Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Ulissi ZW; Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
Blau SM; Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

J Chem Inf Model ; 63(24): 7642-7654, 2023 Dec 25.

Article en En | MEDLINE | ID: mdl-38049389

Asunto(s)

Complejos de Coordinación; Redes Neurales de la Computación; Aprendizaje Automático; Hidrógeno; Termodinámica

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Complejos de Coordinación Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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