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Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

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Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

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Moliner Marin, M.; Román-Leshkov, Y.; Corma Canós, A. (2019). Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Accounts of Chemical Research. 52(10):2971-2980. https://doi.org/10.1021/acs.accounts.9b00399

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/150684

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Title: Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery
Author: Moliner Marin, Manuel Román-Leshkov, Yuriy Corma Canós, Avelino
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] CONSPECTUS: Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined ...[+]
Subjects: Structure-Directing agents , Artificial neural-networks , X-Ray-Diffraction , Hydrothermal synthesis , Molecular-Sieve , Combinatorial , Design , Methodology , Prediction , Frameworks
Copyrigths: Reserva de todos los derechos
Source:
Accounts of Chemical Research. (issn: 0001-4842 )
DOI: 10.1021/acs.accounts.9b00399
Publisher:
American Chemical Society
Publisher version: https://doi.org/10.1021/acs.accounts.9b00399
Project ID: info:eu-repo/grantAgreement/EC/H2020/671093/EU
Thanks:
This work has been supported by the EU through ERC-AdG2014-671093, by the Spanish Government through SEV-20160683 and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE), and by La Caixa-Foundation through MIT -SPAIN MISTI program ...[+]
Type: Artículo

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