Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

Machine learning (ML) predictions for the oxidative coupling of methane (OCM) are evaluated under experiment situation. The ML protocol has sparked new motivation for trial runs of 96 kinds of metal-supported catalysts based not only on scientists' experiences but also on data presented in earlier reports of the literatures and obtained during verification. Our protocol discovers unreported catalyst combinations for OCM reactions from data expanding upon three decades of research, where various numbers of catalysts are predicted and confirmed to perform better than blank data. Nevertheless, the target on C(2)yield for the OCM reaction remains as a challenging subject:i. e. higher than 30 %. Revisiting data reported in the literature reveals that different reactor systems and/or specific methods are used in the original data for achieving higher than 30 % C(2)yield. Such specialties are attributed to the inadequacy of a literature-data-driven ML approach at the present situation. Furthermore, classification of experimental data has indicated target C(2)yield values and trends toward CH(4)and O(2)conversion and product selectivity in high dimensions can improve future ML prediction. These findings are greatly beneficial for the next stage of development to find a global descriptor to improve ML prediction accuracy beyond interpolation filling.