Li Shu-Long, Zhou Hongyuan, Zhou Zuhui, Gan Li-Yong, Cai Fanggong, Zhao Yong, Long Jianping, Qiao Liang
College of Materials and Energy, Guang'an Institute of Technology, Guang'an, Sichuan, 638000, China.
School of Physics, University of Electronic Science and Technology of China, Chengdu, 611700, China.
Small Methods. 2025 Aug 12:e01271. doi: 10.1002/smtd.202501271.
The development of efficient single-atom catalysts (SACs) for electrocatalytic hydrogen evolution (HER) has garnered significant attention within the scientific community. However, the extensive scope of material experimentation, coupled with high research and development costs and prolonged research cycles, severely hampers the efficient advancement of related materials. In this study, the HER activity of 90 types of SACs is systematically investigated, which consist of single transition metal (TM) and/or nonmetal (NM) atoms bonded in graphyne (TM-NM-GY), by synergizing machine learning algorithms with high-throughput DFT computations. The findings reveal that the HER catalytic activity of Fe-GY, Fe-B-GY, Ni-B-GY, Pd-B-GY, Sc-N-GY, Co-N-GY, Y-N-GY, and Pd-N-GY surpasses that of commercial Pt/C catalysts. Moreover, non-metallic B or N atom doping can effectively modulate the HER performance of SACs. Furthermore, it is confirmed that HER activity correlates with characteristic factors such as the bond length of the coordinating atoms, d-band center, metal binding height, charge transfer, and ICOHP. Finally, machine learning stacking models have proven efficient in predicting and designing superior HER SACs. It is anticipated that these insights will accelerate the prediction and design of corresponding SACs.
用于电催化析氢反应（HER）的高效单原子催化剂（SAC）的开发在科学界引起了广泛关注。然而，材料实验范围广泛，加上研发成本高和研究周期长，严重阻碍了相关材料的高效发展。在本研究中，通过将机器学习算法与高通量密度泛函理论（DFT）计算相结合，系统地研究了90种由单过渡金属（TM）和/或非金属（NM）原子键合在石墨炔中的SAC（TM-NM-GY）的HER活性。研究结果表明，Fe-GY、Fe-B-GY、Ni-B-GY、Pd-B-GY、Sc-N-GY、Co-N-GY、Y-N-GY和Pd-N-GY的HER催化活性超过了商业Pt/C催化剂。此外，非金属B或N原子掺杂可以有效调节SAC的HER性能。此外，证实HER活性与配位原子的键长、d带中心、金属结合高度、电荷转移和内聚能（ICOHP）等特征因素相关。最后，机器学习堆叠模型已被证明在预测和设计优异的HER SAC方面是有效的。预计这些见解将加速相应SAC的预测和设计。
