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Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication.
Blay, Vincent; Yokoi, Toshiyuki; González-Díaz, Humbert.
Afiliación
  • Blay V; Fisher College of Business , The Ohio State University , Gerlach Hall, 2108 Neil Avenue, Columbus , Ohio 43210 , United States.
  • Yokoi T; Institute of Innovative Research, Chemical Resources Laboratory , Tokyo Institute of Technology , 4259 Nagatsuta , Midori-ku, Yokohama 226-8503 , Japan.
  • González-Díaz H; Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.
J Chem Inf Model ; 58(12): 2414-2419, 2018 12 24.
Article en En | MEDLINE | ID: mdl-30139249
ABSTRACT
Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory and Machine Learning can be combined in a PTML multioutput model describing the effects of desilication. The PTML model achieves a notable accuracy ( R2 = 0.98) in the external validation and can be useful for the rational design of novel materials.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Silicio / Zeolitas / Aprendizaje Automático Tipo de estudio: Health_economic_evaluation Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Silicio / Zeolitas / Aprendizaje Automático Tipo de estudio: Health_economic_evaluation Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos