@article{Zhou2024, 
author = {Lei Zhou and Pengfei Tian and Bowei Zhang and Fu-Zhen Xuan},
title = {Data-driven rational design of single-atom materials for hydrogen evolution and sensing},
year = {2024},
journal = {Nano Research},
volume = {17},
number = {4},
pages = {3352-3358},
keywords = {machine learning, sensing, hydrogen evolution, single-atom materials},
url = {https://www.sciopen.com/article/10.1007/s12274-023-6137-5},
doi = {10.1007/s12274-023-6137-5},
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.}
}