Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery-Reference-Cited by-同舟云学术

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

Published:2019-09-25 Issue:10 Volume:52 Page:2971-2980
ISSN:0001-4842
Container-title:Accounts of Chemical Research
language:en
Short-container-title:Acc. Chem. Res.

Author:

Moliner Manuel¹^ORCID,Román-Leshkov Yuriy²^ORCID,Corma Avelino¹^ORCID

Affiliation:

1. Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022 València, Spain

2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

Funder

Basic Energy Sciences

?la Caixa? Foundation

H2020 European Research Council

Spanish Government

Publisher

American Chemical Society (ACS)

Subject

General Medicine,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.accounts.9b00399

Reference58 articles.

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3. Towards the Rational Design of Efficient Organic Structure-Directing Agents for Zeolite Synthesis

4. Machine learning: Trends, perspectives, and prospects

5. Schultz, P. G.; Xiang, X.; Goldwasser, I. U.S. Patent US5,985,356, 1994.

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