Application of artificial neural networks to combinatorial catalysis: modeling and predicting ODHE catalysts

This paper shows how artificial neural networks are useful for modeling catalytic data from combinatorial catalysis and for predicting new potential catalyst compositions for the oxidative dehydrogenation of ethane (ODHE). The training and testing sets of data used for the neural network studies were obtained by means of a combinatorial approach search, which employs an evolutionary optimization strategy. Input and output variables of the neural network include the molar composition of thirteen different elements presented in the catalyst and five catalytic performances (C2H6 and O2 conversion, C2H4 yield, and C2H4, CO2, and CO selectivity). The fitting results indicate that neural networks can be useful in high-dimensional data management within combinatorial catalysis search procedures, since neural networks allow the ab initio evaluation of the reactivity of multicomponent catalysts.