2024-03-29T15:39:28Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/2095352021-12-28T15:48:05Zcom_10261_99com_10261_5col_10261_352
Jensen, Z
Kim, E.
Kwon, S.
Gani, T.Z.H.
Román-Leshkov, Y
Moliner Marín, Manuel
Corma, Avelino
Olivetti, E.
2020-04-29T07:23:13Z
2020-04-29T07:23:13Z
2019-04-19
ACS central science 5(5): 892-899 (2019)
http://hdl.handle.net/10261/209535
10.1021/acscentsci.9b00193
http://dx.doi.org/10.13039/501100003329
31139725
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
, 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.
https://pubs.acs.org/page/policy/authorchoice_termsofuse.html
openAccess
A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
artículo