Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane
Sora Ishioka, Aya Fujiwara, Sunao Nakanowatari, Lauren Takahashi, Toshiaki Taniike, Keisuke Takahashi
Abstract
Catalysts descriptors for representing catalytic activities have been challenging in regard to machine learning. Machine learning and catalyst big data generated from high-throughput experiments are combined to explore the catalyst descriptors. Catalyst descriptors are designed using the physical quantities from the periodic table in the oxidative coupling of methane (OCM) reaction. Machine learning unveils the five key physical quantities representing ethylene/ethane selectivity (C2s) in the OCM reaction, where machine learning predicted three catalysts to have high C2s values. Experiments confirm that the proposed three catalysts have high C2s values in the OCM reaction. Hence, the physical quantities can be used as alternative descriptors for designing heterogeneous catalysts.