Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication

Vincent Blay; Toshiyuki Yokoi; Humbert González-Díaz

doi:10.1021/acs.jcim.8b00383

Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication

Vincent Blay, ¹ Toshiyuki Yokoi, ² Humbert González-Díaz ³

¹ Fisher College of Business, The Ohio State University, Gerlach Hall, 2108 Neil Avenue, Columbus, Ohio 43210, United States
² Institute of Innovative Research, Chemical Resources Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
³ Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Spain

Published: 23 August 2018 by American Chemical Society (ACS) in Journal of Chemical Information and Modeling

American Chemical Society (ACS), Volume 58; 10.1021/acs.jcim.8b00383

Keywords: optimization, model, machine learning, Perturbation, zeolite, Desilication, Multioutput, Theory Machine, PTML

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Vincent Blay

Toshiyuki Yokoi

Humbert González-Díaz