Generalizability and limitations of machine learning for yield prediction of oxidative coupling of methane

Product yields of catalytic reaction networks are dependent on many factors, encompassing both catalyst properties and reaction conditions. The oxidative coupling of methane (OCM) is a complex heterogeneous-homogeneous process, and the yield of the desired C 2 products is non-linear with respect to reaction conditions. Herein, using two published datasets of OCM catalytic experimental results, I show that various machine learning (ML) algorithms can predict C 2 yields from reaction conditions with a mean absolute error (MAE) of 0.5 - 1.0 percentage points in the best case. However, complications arising from real-world applications should be anticipated, therefore I investigated the effects of training set size, added noise, and out-of-sample partitions on the performance of ML algorithms. These results provide insights into the generalizability of the algorithms as well as caveats into the applicability of ML to reaction yield prediction