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Nano Research 2024, 17(4): 3352-3358 https://doi.org/10.1007/s12274-023-6137-5
Research Article | Issue | Published: 28 October 2023

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

Show Author's Information Lei Zhou1,2,3,  Pengfei 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
Keywords:
machine learning, sensing, hydrogen evolution, single-atom materials
Topics:
Chemical sensors HydrogenHydrogen evolution reaction
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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Abstract Full text Electronic supplementary material About this article

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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About this article

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

Show Author's information Lei Zhou1,2,3, Pengfei 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

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.

Keywords: machine learning, sensing, hydrogen evolution, single-atom materials

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Publication history
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Acknowledgements

Publication history

Received: 03 August 2023
Revised: 20 August 2023
Accepted: 22 August 2023
Published: 28 October 2023
Issue date: April 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 52105145 and 12274124), the Shanghai Pilot Program for Basic Research (No. 22TQ1400100-6), and the Fundamental Research Funds for the Central Universities.

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