Analysis of factors controlling catalytic activity by neural network

An artificial neural network was applied to the analysis of factors controlling catalytic activity by taking, as examples, experimentally established correlations of catalytic activities with primary factors including both monotonous and volcano-type correlations. Three equations were proposed and applied to the estimation of relative importance of each given factor from the weightings of connecting links in the trained artificial neural network of an error back-propagation model. In all the examples, the primary factors that had been proposed in experimental studies were successfully identified by using an equation based on our previous proposal. Further, the possibility of identifying secondary factor was also discussed.