Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species

Abstract

Catalysts for oxidative coupling of methane (OCM) were designed through machine learning of a property of surface oxygen species on the basis of the knowledge that catalytic performance for the OCM is affected by catalyst surface oxygen species. To select the property of the surface oxygen species used as a guide of catalyst design via machine learning, the relationships between the total yield of ethylene and ethane (C₂ yield) and the O1s X-ray photoelectron spectral (XPS) features of the 51 catalysts prepared in our previous study were evaluated. Since a weak correlation was seen between the C₂ yield and the O1s XPS peak energy of CO₃²⁻ species on the catalyst surface, the CO₃²⁻ peak energy was chosen as the guiding parameter of catalyst design in this work. Machine learning was then performed on the dataset consisting of the CO₃²⁻ peak energy (objective variable) and the physical quantities of elements in the catalysts (descriptor) to find the important physical quantities determining the CO₃²⁻ peak energy. According to the important physical quantities, catalyst compositions were predicted. Based on the predicted compositions, 28 catalysts were synthesized to verify that their CO₃²⁻ peak energies were in the range where high catalytic performance can be expected. Furthermore, the catalysts are tested for the OCM reaction. As a result, Ba–In–Rb/La₂O₃ was found as a new highly active OCM catalyst having compatible activity to the conventional Mn–Na₂WO₄/SiO₂ catalyst. Therefore, it was demonstrated that the indirect catalyst through machine learning of the catalyst surface property is effective for development of catalysts.