Machine-Learning-Assisted Discovery of High-Efficient Oxygen Evolution Electrocatalysts-Reference-Cited by-同舟云学术

Machine-Learning-Assisted Discovery of High-Efficient Oxygen Evolution Electrocatalysts

Published:2022-12-29 Issue:1 Volume:14 Page:170-177
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Mao Xinnan¹,Wang Lu¹^ORCID,Li Youyong¹²^ORCID

Affiliation:

1. Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu215123, People’s Republic of China

2. Macao Institute of Materials Science and Engineering, Macau University of Science and Technology, Taipa, Macau999078, People’s Republic of China

Funder

Collaborative Innovation Center of Suzhou Nano Science and Technology

Natural Science Foundation of Jiangsu Province

Fundo para o Desenvolvimento das Ci?ncias e da Tecnologia

National Natural Science Foundation of China

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.2c02873

Reference59 articles.

1. Oxygen Electrochemistry as a Cornerstone for Sustainable Energy Conversion

2. Design of electrocatalysts for oxygen- and hydrogen-involving energy conversion reactions

3. Electrocatalysis for the oxygen evolution reaction: recent development and future perspectives

4. Robust noble metal-based electrocatalysts for oxygen evolution reaction

5. A review on fundamentals for designing oxygen evolution electrocatalysts

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