Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

To demonstrate the viability of our method, we predicted reaction outcomes with substrate combinations and catalysts different from the training data and simulated a situation in which highly selective reactions had not been achieved. In the first demonstration, a model was constructed by using support vector machines and validated with three different external test sets. The first test set evaluated the ability of the model to predict the selectivity of only reactions forming new products with catalysts from the training set. The model performed well, with a mean absolute deviation (MAD) of 0.161 kcal/mol. Next, the same model was used to predict the selectivity of an external test set of catalysts with substrate combinations from the training set.