Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 2

作者：Yuan Jing, Chenyang Zhang, Shinya Mine, Xiupeng Zhang, Chenxi He, Ningqiang Zhang, Xu Guo, Akihiko Anzai, Kohei Oka, Ryo Toyoshima, Hiroshi Kondoh, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao · 发表于：ACS Catalysis · 年份：2025 · DOI：10.1021/acscatal.5c06074 · 被引用次数：8 · 研究领域：Ammonia Synthesis and Nitrogen Reduction、Catalytic Processes in Materials Science、Machine Learning in Materials Science

Selective catalytic reduction of NO x with hydrogen (H 2 –SCR) in the presence of oxygen is an environmentally friendly technology that has attracted considerable attention. However, even the most promising currently available catalysts are not sufficiently active to effectively promote this reaction, particularly at low temperatures (<150 °C). Therefore, there is an urgent need for the development of highly active H 2 –SCR catalysts. Although data-science approaches, including machine learning (ML), have been suggested to accelerate the development of catalysts for such important processes, the discovery of efficient catalysts using ML remains limited. This limitation stems from a common criticism of ML, namely, its perceived inability to extrapolate and identify extraordinary materials. Herein, we present an extrapolative ML approach for the development of efficient multielemental H 2 –SCR catalysts. Starting with 45 catalysts as the initial dataset, we employed a closed-loop discovery system that combined ML predictions and experimental validation over 24 iterative cycles. The iterative workflow facilitated the experimental evaluation of 425 catalysts, leading to the discovery of several compositions surpassing previously reported systems in average N 2 yield within 50–150 °C. The top-performing catalyst was identified as Pt(1.3)–Ir(0.2)/Ba(1.5)–Co(1)/H-ZSM-5 (Si/Al ratio = 11). Notably, the optimal catalyst contained Co, an element absent from the initial dataset; the opt...