The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning-Reference-Cited by-同舟云学术

The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning

Published:2023 Issue:28 Volume:25 Page:18983-18989
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Chen Ziqiang¹,Qi Hexiang¹,Wang Haohao¹,Yue Caiwei¹,Liu Yangqiu¹,Yang Zuoyin¹,Pu Min¹^ORCID,Lei Ming¹^ORCID

Affiliation:

1. State Key Laboratory of Chemical Resource Engineering, Institute of Computational Chemistry, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China

Abstract

Two descriptors valence electron correction VEc and degree of construction differences DC were proposed to improve the machine learning prediction accuracy of single-atom graphene-based systems (ZZ/ZA-MNxCy).

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP01224G

Reference42 articles.

1. Advanced Biomass‐Derived Electrocatalysts for the Oxygen Reduction Reaction

2. Unraveling electrochemical oxygen reduction mechanism on single‐atom catalysts by a computational investigation

3. First-Principles Study of Pt-Based Bifunctional Oxygen Evolution & Reduction Electrocatalyst: Interplay of Strain and Ligand Effects

4. Enhanced oxygen reduction reaction performance of ReOx/NC (Re = La, Ce, Pr, Sm, Eu, Tb, Er, Tm and Yb)-especially Pr6O11/NC via accommodating oxygen vacancies and its application for Zn-air battery

5. Theoretical investigation of the ORR on boron–silicon nanotubes (B–SiNTs) as acceptable catalysts in fuel cells

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