Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO<sub>2</sub> Reduction Reaction-Reference-Cited by-同舟云学术

Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO₂ Reduction Reaction

Published:2022-11-22 Issue:24 Volume:12 Page:14864-14871
ISSN:2155-5435
Container-title:ACS Catalysis
language:en
Short-container-title:ACS Catal.

Author:

Chen Zhi Wen¹,Gariepy Zachary¹,Chen Lixin¹,Yao Xue¹,Anand Abu¹,Liu Szu-Jia¹,Tetsassi Feugmo Conrard Giresse²^ORCID,Tamblyn Isaac³^ORCID,Singh Chandra Veer¹⁴^ORCID

Affiliation:

1. Department of Materials Science and Engineering, University of Toronto, 184 College Street, Suite 140, Toronto, Ontario M5S 3E4, Canada

2. National Research Council of Canada, Ottawa, Ontario K1A 0R6, Canada

3. Department of Physics, University of Ottawa, Vector Institute for Artificial Intelligence, Ottawa, Ontario K1N 6N5, Canada

4. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada

Funder

University of Toronto

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Subject

Catalysis,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acscatal.2c03675

Reference44 articles.

1. A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels

2. Accelerated discovery of CO2 electrocatalysts using active machine learning

3. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution

4. Strategies to break linear scaling relationships

Cited by 33 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High entropy alloy electrocatalysts;Journal of Energy Chemistry;2024-12

2. Theoretical modulation of Cu-based ternary alloys for the selectivity of electrochemical reduction of carbon dioxide;Chemical Engineering Science;2024-10

3. High-entropy materials: Excellent energy-storage and conversion materials in the field of electrochemistry;Particuology;2024-09

4. Accelerating electrocatalyst design for CO2 conversion through machine learning: Interpretable models and data-driven innovations;Nexus;2024-09

5. Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials;The Journal of Physical Chemistry C;2024-07-02