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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/150669

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Title: A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Author: Jensen, Zach Kim, Edward Kwon, Soonhyoung Gani, Terry Z.H. Román-Leshkov, Yuriy Moliner Marin, Manuel Corma Canós, Avelino Olivetti, Elsa
UPV Unit: Universitat Politècnica de València. Instituto Universitario Mixto de Tecnología Química - Institut Universitari Mixt de Tecnologia Química
Universitat Politècnica de València. Departamento de Química - Departament de Química
Issued date:
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 ...[+]
Subjects: Large-Pore zeolite , Molecular-Sieves , Framework-Density , Crystallization , Discovery , Location , Design , Ion
Copyrigths: Reconocimiento - No comercial (by-nc)
Source:
ACS Central Science. (eissn: 2374-7951 )
DOI: 10.1021/acscentsci.9b00193
Publisher:
American Chemical Society
Publisher version: https://doi.org/10.1021/acscentsci.9b00193
Thanks:
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. ...[+]
Type: Artículo

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