Predicting the outcome of a catalytic reaction is of relevance to high-throughput screening of chemical reactions for industrial applications.  Here we show that machine learning can be used to accurately predict the outcomes of catalytic reactions 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.

Machine learning catalytic reactions

2019 Fall National Meeting

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