English
español
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10261/209535
Share/Impact:
Statistics |
![]() ![]() ![]() |
|
|
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |||
|
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jensen, Z | - |
dc.contributor.author | Kim, E. | - |
dc.contributor.author | Kwon, S. | - |
dc.contributor.author | Gani, T.Z.H. | - |
dc.contributor.author | Román-Leshkov, Y | - |
dc.contributor.author | Moliner Marín, Manuel | - |
dc.contributor.author | Corma, Avelino | - |
dc.contributor.author | Olivetti, E. | - |
dc.date.accessioned | 2020-04-29T07:23:13Z | - |
dc.date.available | 2020-04-29T07:23:13Z | - |
dc.date.issued | 2019-04-19 | - |
dc.identifier | doi: 10.1021/acscentsci.9b00193 | - |
dc.identifier | issn: 2374-7951 | - |
dc.identifier.citation | ACS central science 5(5): 892-899 (2019) | - |
dc.identifier.uri | http://hdl.handle.net/10261/209535 | - |
dc.description.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 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. | - |
dc.description.sponsorship | We would like to acknowledge funding from the National Science Foundation Award No. 1534340, DMREF that provided support to make this work possible, support from the Office of Naval Research (ONR) under Contract No. N00014-16-1-2432, and the MIT Energy Initiative. Early work was collaborative under the Department of Energy Basic Energy Science Program through the Materials Project under Grant No. EDCBEE. This work has also been supported by the Spanish Government through the Severo Ochoa Program SEV2016-0683 and the Grant No. MAT2015971261-R, and by La Caxia Foundation through the MIT-SPAIN SEED FUND Program (LCF/PR/MIT17/11820002) | - |
dc.language | eng | - |
dc.publisher | ACS Publications | - |
dc.relation | MINECO/ICTI2013-2016/SEV-2016-0683 | - |
dc.relation | MINECO/ICTI2013-2016/MAT2015-71261-R | - |
dc.relation.isversionof | Publisher's version | - |
dc.rights | openAccess | - |
dc.title | A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction | - |
dc.type | artículo | - |
dc.identifier.doi | http://dx.doi.org/10.1021/acscentsci.9b00193 | - |
dc.relation.publisherversion | http://dx.doi.org/10.1021/acscentsci.9b00193 | - |
dc.date.updated | 2020-04-29T07:23:14Z | - |
dc.rights.license | https://pubs.acs.org/page/policy/authorchoice_termsofuse.html | - |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | - |
dc.relation.csic | Sí | - |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003329 | es_ES |
Appears in Collections: | (ITQ) Artículos |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
A Machine Learning Approach to Zeolite Synthesis... acscentsci.9b00193.pdf | 1,29 MB | Adobe PDF | ![]() View/Open |
Show simple item record
WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.