High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane

• Published in: Catalysis science & technology Vol. 12; no. 9; pp. 2766 - 2774
• Main Authors: Nishimura, Shun, Son Dinh Le, Miyazato, Itsuki, Fujima, Jun, Taniike, Toshiaki, Ohyama, Junya, Takahashi, Keisuke
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 10.05.2022
• Subjects: Catalysts; Coupling; Data mining; Gaussian process; Lanthanum oxides; Machine learning; Methane; Regression; Screening; Support vector machines

Herein, multi-component La2O3-based catalysts for the oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR), support vector regression (SVR), Gauss...