Data-driven catalyst design for direct catalytic N2O decomposition

作者：Chenxi He, Shinya Mine, Yuan Jing, Tsz Lok Wan, Jialei Zhang, Junxian Qin, Ningqiang Zhang, Koichiro Taketoshi, Akihiko Anzai, Ryo Toyoshima, Hiroshi KONDOH, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao · 发表于：Nature Communications · 年份：2026 · DOI：10.1038/s41467-026-75902-z · 被引用次数：2 · 研究领域：Catalytic Processes in Materials Science、Catalysis and Oxidation Reactions

Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employ a machine learning approach designed to accelerate the discovery of effective direct N2O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (machine-learning prediction + experiment), 633 catalysts are experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity are identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO2_EP exhibits the highest catalytic performance for N2O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identify the essential role of each component within the catalyst system. Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, yet identifying efficient catalysts for this reaction remains challenging. Here, the authors employ a machine learning approach to accelerate the discovery of effective catalysts for direct N₂O decomposition.