Machine learning-aided understanding of the structure–activity relationship: a case study of MoS<sub>2</sub> supported metal–nonmetal pairs for the hydrogen evolution reaction-Reference-Cited by-同舟云学术

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Machine learning-aided understanding of the structure–activity relationship: a case study of MoS₂ supported metal–nonmetal pairs for the hydrogen evolution reaction

Published:2024 Issue: Volume: Page:
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Chen Anjie¹,Sun Jinxin¹,Guan Junming¹,Liu Yaqi¹,Han Ying¹,Zhou Wenqi¹,Zhao Xinli¹,Wang Yanbiao²,Liu Yongjun¹,Zhang Xiuyun¹³^ORCID

Affiliation:

1. College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China

2. Department of Fundamental Courses, Wuxi Institute of Technology, Wuxi 214121, China

3. Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 211189, China

Abstract

Understanding the structure–performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge.

Funder

Six Talent Peaks Project in Jiangsu Province

Southeast University

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/NR/D4NR02112F

Reference56 articles.

1. Towards greener and more sustainable batteries for electrical energy storage

2. Research advances of light-driven hydrogen evolution using polyoxometalate-based catalysts

3. Electrocatalyst approaches and challenges for automotive fuel cells

4. Combining theory and experiment in electrocatalysis: Insights into materials design

5. In situ/operando vibrational spectroscopy for the investigation of advanced nanostructured electrocatalysts

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