A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Name
acscentsci.9b00193.pdf
Description
Published version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
9d5da5040d0c5f5b4718154363c397f4
Author(s)
Jensen, Zach
Kim, Edward
Kwon, Soonhyoung
Gani, Terry ZH
Román-Leshkov, Yuriy
Moliner, Manuel
Corma, Avelino
Olivetti, Elsa
Date Issued
2019
Journal
ACS Central Science
Publisher
American Chemical Society (ACS)
Version
Final published version
Abstract
© 2019 American Chemical Society. 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 Å 3 , 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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acscentsci.9b00193
