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Research Article

Data-driven rational design of single-atom materials for hydrogen evolution and sensing

Lei Zhou1,2,3Pengfei Tian1,2,3( )Bowei Zhang1,2,3( )Fu-Zhen Xuan1,2,3( )
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai 200237, China
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China
Key Laboratory of Pressure Systems and Safety of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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Graphical Abstract

Single atom materials have shown great potential in the field of hydrogen evolution and sensing. Herein, we used machine learning to help screen high-performance single atom materials toward electrochemical hydrogen evolution and hydrogen sensing.

Abstract

Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction (HER) and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning algorithm. After the rational training by 25 groups of data and prediction of all 168 groups of single-atom materials for HER and sensing, respectively, a high prediction accuracy (> 0.931 R2 score) was achieved by our model. Results show that the promising HER catalysts include Pt atoms in C4 and Sc atoms in C1N3 coordination environment. Moreover, Y atoms in C4 coordination environment and Cd atoms in C2N2-ortho coordination environment were predicted with great potential as hydrogen sensing materials. This method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.

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Nano Research
Pages 3352-3358
Cite this article:
Zhou L, Tian P, Zhang B, et al. Data-driven rational design of single-atom materials for hydrogen evolution and sensing. Nano Research, 2024, 17(4): 3352-3358. https://doi.org/10.1007/s12274-023-6137-5
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Received: 03 August 2023
Revised: 20 August 2023
Accepted: 22 August 2023
Published: 28 October 2023
© Tsinghua University Press 2023
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