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A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

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A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

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dc.contributor.author Jensen, Zach es_ES
dc.contributor.author Kim, Edward es_ES
dc.contributor.author Kwon, Soonhyoung es_ES
dc.contributor.author Gani, Terry Z.H. es_ES
dc.contributor.author Román-Leshkov, Yuriy es_ES
dc.contributor.author Moliner Marin, Manuel es_ES
dc.contributor.author Corma Canós, Avelino es_ES
dc.contributor.author Olivetti, Elsa es_ES
dc.date.accessioned 2020-09-24T12:30:08Z
dc.date.available 2020-09-24T12:30:08Z
dc.date.issued 2019-05-22 es_ES
dc.identifier.uri http://hdl.handle.net/10251/150669
dc.description.abstract [EN] 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 angstrom(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. es_ES
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 SEV-2016-0683 and the Grant No. MAT2015971261-R, and by La Caxia Foundation through the MIT-SPAIN SEED FUND Program (LCF/PR/MIT17/11820002). es_ES
dc.language Inglés es_ES
dc.publisher American Chemical Society es_ES
dc.relation.ispartof ACS Central Science es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Large-Pore zeolite es_ES
dc.subject Molecular-Sieves es_ES
dc.subject Framework-Density es_ES
dc.subject Crystallization es_ES
dc.subject Discovery es_ES
dc.subject Location es_ES
dc.subject Design es_ES
dc.subject Ion es_ES
dc.subject.classification QUIMICA ORGANICA es_ES
dc.title A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1021/acscentsci.9b00193 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//MAT2015-71261-R/ES/DISEÑO RACIONAL DE MATERIALES ZEOLITICOS CON CENTROS METALICOS PARA SU APLICACION EN PROCESOS QUIMICOS SOSTENIBLES, MEDIOAMBIENTALES Y ENERGIAS RENOVABLES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSF//1534340/US/DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ONR//N00014-16-1-2432/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FMIT17%2F11820002/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//SEV-2016-0683/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto de Tecnología Química - Institut Universitari Mixt de Tecnologia Química es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Química - Departament de Química es_ES
dc.description.bibliographicCitation Jensen, Z.; Kim, E.; Kwon, S.; Gani, TZ.; Román-Leshkov, Y.; Moliner Marin, M.; Corma Canós, A.... (2019). A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science. 5(5):892-899. https://doi.org/10.1021/acscentsci.9b00193 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1021/acscentsci.9b00193 es_ES
dc.description.upvformatpinicio 892 es_ES
dc.description.upvformatpfin 899 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.description.issue 5 es_ES
dc.identifier.eissn 2374-7951 es_ES
dc.identifier.pmid 31139725 es_ES
dc.identifier.pmcid PMC6535764 es_ES
dc.relation.pasarela S\406638 es_ES
dc.contributor.funder Office of Naval Research es_ES
dc.contributor.funder U.S. Department of Energy es_ES
dc.contributor.funder National Science Foundation, EEUU es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Fundació Bancària Caixa d'Estalvis i Pensions de Barcelona es_ES


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