Abstract:
Demand of ethylene is surging at unprecedented rate and therefore conversion of methane into
these important value-added chemicals using one-step processes such oxidative coupling of
methane is of paramount importance. It would be beneficial and significant to design a catalyst
which can suppress undesired over reactions which lead to the production of COx gases
resulting in issues of yield and selectivity. To this end, in this study, Machine learning (ML)
models integrated with genetic algorithm (GA) have been developed to predict, evaluate, and
analyze adsorption energies of methane related species on Cu-based Alloys. Comparative study
of different ML algorithms integrated with Genetic Algorithm (GA) were performed to
improve the ML model’s architecture and parameters selection. The results proposed that
Categorical boosting (Catboost) model outperformed all other models and effectively predicts
adsorption energies compared to other models (RMSE = 0.0977, CC = 96.5 %). Permutation
importance score was utilized to asses prediction performance and accuracy which revealed
that group number and surface energy contributed the highest to the model. The partial
dependence plots (PDPs) analysis shows the potential effects of each influencing parameter
impact on the prediction of the respective adsorption energies and as well as shows that how
these factors will interact during oxidative coupling of methane (OCM). Finally, in order to
analyze the distribution of the data points of the features and how they affect the model’s
predictions, bee swarm plot was employed.
