Xu Liangliang, Wang Xingkun, Hu Xiaojuan, Wang Yue, Zhang Canhui, Xu Wenwu, Zhao Wenhui, Xu Ning, Woo Dongyoon, Yao Hanxu, Li Xiaofan, Jiang Heqing, Huang Minghua, Lee Jinwoo, Zeng Xiao Cheng, Han Zhong-Kang
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-Ro, Yuseong-Gu, Daejeon 34141, Republic of Korea.
School of Materials Science and Engineering, Ocean University of China, Qingdao, China.
Innovation (Camb). 2025 Apr 17;6(7):100911. doi: 10.1016/j.xinn.2025.100911. eCollection 2025 Jul 7.
Artificial intelligence (AI)-assisted approaches are powerful means for advancing catalyst design, as they can significantly accelerate the development of novel catalysts. However, the underlying mechanisms of these approaches often remain elusive, which may lead to unreliable results due to a lack of clear understanding of the involved processes. Herein, we present an AI strategy that combines machine learning (ML) and data mining (DM) to identify high-performance catalysts while elucidating the key factors that govern catalytic performance in complex reactions. Applying this AI strategy to evaluate the electrocatalytic oxygen reduction performance of 10,179 single-atom catalysts (SACs), we identified several high-performance SACs and determined the critical influencers of their activity. Experimental validations further confirm the effectiveness of the AI strategy, with the optimal target Co-SN/g-SAC achieving a high half-wave potential of 0.92 V. This AI-assisted approach significantly enhances the transparency and reliability of data-driven discoveries, providing new insights that benefit the rational design of materials.
人工智能（AI）辅助方法是推进催化剂设计的有力手段，因为它们可以显著加速新型催化剂的开发。然而，这些方法的潜在机制往往仍然难以捉摸，由于对所涉及的过程缺乏清晰的理解，这可能导致不可靠的结果。在此，我们提出一种将机器学习（ML）和数据挖掘（DM）相结合的人工智能策略，以识别高性能催化剂，同时阐明在复杂反应中控制催化性能的关键因素。将这种人工智能策略应用于评估10179种单原子催化剂（SAC）的电催化氧还原性能，我们识别出了几种高性能SAC，并确定了其活性的关键影响因素。实验验证进一步证实了该人工智能策略的有效性，最优目标Co-SN/g-SAC实现了0.92 V的高半波电位。这种人工智能辅助方法显著提高了数据驱动发现的透明度和可靠性，提供了有益于材料合理设计的新见解。
