Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys-Reference-Cited by-同舟云学术

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

Published:2023 Issue:26 Volume:15 Page:11072-11082
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Zhang Jingzi¹²^ORCID,Wang Yuelin¹²,Zhou Xuyan¹²,Zhong Chengquan¹²,Zhang Ke¹²,Liu Jiakai¹²,Hu Kailong¹²³,Lin Xi¹²³

Affiliation:

1. School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China

2. Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China

3. State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

Abstract

By incorporating electronic and structural properties, an efficient ML model was constructed to predict high-performance HER alloy catalysts, with enhancement in the computational efficiency for potential alloy catalysts across a vast chemical space.

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Putian University

Shenzhen Science and Technology Innovation Program

Development and Reform Commission of Shenzhen Municipality

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NR/D3NR01442H

Reference57 articles.