Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 2

Methane (CH4) combustion under lean conditions is a critical reaction for controlling unburned hydrocarbon emissions in natural gas engines. However, the development of highly active and sulfur-tolerant catalysts remains a major challenge due to the severe deactivation caused by sulfur compounds such as SO2. In this study, we adopted a machine-learning (ML) -guided strategy to accelerate the discovery of CH4 combustion catalysts that are tolerant to sulfur poisoning. Starting from 16 initial catalysts and conducting 24 cycles of a closed-loop discovery system (ML prediction + experiment), a total of 300 multielemental catalysts were experimentally evaluated under identical conditions in the presence of SO2. Through this approach, over 30 catalysts exhibiting high CH4 conversion and excellent sulfur tolerance were identified. Among them, Pd (2) -Ru (0. 4) -Ir (0. 3) -Pt (0. 3) /ZrO2JRC3RC-100 demonstrated the highest catalytic performance. Control experiments and comprehensive characterizations, including in situ/operando spectroscopy, revealed the individual and synergistic roles of each component in enhancing both activity and resistance to sulfur poisoning.