Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning-Reference-Cited by-同舟云学术

Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning

Published:2024 Issue:14 Volume:26 Page:11037-11047
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Zhang Qing-Meng¹,Wang Zhao-Yu¹,Zhang Hao¹,Liu Xiao-Hong²³,Zhang Wei²,Zhao Liu-Bin¹^ORCID

Affiliation:

1. Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing, 400715, China

2. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China

3. National University of Singapore (Chongqing) Research Institute, Chongqing 401123, China

Abstract

Density functional theory calculations are combined with machine learning for rapid and high-throughput screening of high performance single-atom catalysts for the CO reduction reaction.

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Natural Science Foundation of Chongqing Municipality

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/CP/D4CP00325J

Reference72 articles.

1. Optimizing the Electrocatalytic Selectivity of Carbon Dioxide Reduction Reaction by Regulating the Electronic Structure of Single‐Atom M‐N‐C Materials

2. Electrocatalysis for CO2 conversion: from fundamentals to value-added products

3. Theoretical Insights into Heterogeneous (Photo)electrochemical CO2 Reduction

4. Electrochemical CO 2 Reduction into Chemical Feedstocks: From Mechanistic Electrocatalysis Models to System Design

5. Catalyst Design for Electrochemical Reduction of CO 2 to Multicarbon Products

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