Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

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.