Predicting outcomes of catalytic reactions using machine learning

Predicting the outcome of a catalytic reaction is of relevance to high-throughput screening of chemical reactions for industrial applications. High-throughput screening can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. In this talk we show that machine learning can be used to accurately predict the outcomes of catalytic reactions on the surface of oxygen-covered and bare gold in a database. Our machine learning models exploit a chemical space representation of the molecules in the database. Studying the catalytic reactions in this chemical space may provide insights into their behavior. Furthermore, our approach provides a framework for performing high-throughput screening of chemical reactions, as well as venues for pursuing the inverse design of industrially relevant molecules. Our machine learning framework complements chemical intuition in predicting the outcome of several types of chemical reactions. In some cases, machine learning makes correct predictions where chemical intuition fails. We achieve up to 93% prediction accuracy for a small data set of less than two hundred reactions.