Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening-Reference-Cited by-同舟云学术

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Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

Published:2020-03-19 Issue:9 Volume:11 Page:3185-3191
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Gu Geun Ho¹,Noh Juhwan¹,Kim Sungwon¹,Back Seoin²^ORCID,Ulissi Zachary³^ORCID,Jung Yousung¹^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea

2. Department of Chemical and Biomolecular Engineering, Sogang University, Seoul 04107, South Korea

3. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United States

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.0c00634

Reference55 articles.

1. Nobel Prize in chemistry 1912 to Sabatier: Organic chemistry or catalysis?

2. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis

3. Computational Screening of Doped α‐MnO 2 Catalysts for the Oxygen Evolution Reaction

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