Your browser doesn't support javascript.
loading
A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction.
Jensen, Zach; Kim, Edward; Kwon, Soonhyoung; Gani, Terry Z H; Román-Leshkov, Yuriy; Moliner, Manuel; Corma, Avelino; Olivetti, Elsa.
Afiliação
  • Jensen Z; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Kim E; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Kwon S; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Gani TZH; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Román-Leshkov Y; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Moliner M; Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022 Valencia, Spain.
  • Corma A; Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022 Valencia, Spain.
  • Olivetti E; Department of Materials Science and Engineering and Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
ACS Cent Sci ; 5(5): 892-899, 2019 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-31139725
ABSTRACT
Zeolites are porous, aluminosilicate materials with many industrial and "green" applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite's framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 Å3, and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies.

Similares

MEDLINE

...
LILACS

LIS

Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Inglês Revista: ACS Cent Sci Ano de publicação: 2019 Tipo de documento: Artigo País de afiliação: Estados Unidos