Predicting outcomes of catalytic reactions using machine learning

TL;DR

This paper presents a machine learning model that predicts catalytic reaction outcomes with high accuracy, reducing experimental efforts and complementing chemical intuition, especially in complex reaction scenarios.

Contribution

The study introduces a novel machine learning approach that maps reactants to products using chemical space representations, improving prediction accuracy over traditional methods.

Findings

Achieves up to 93% prediction accuracy on small datasets

Successfully predicts reaction outcomes where chemical intuition fails

Provides a computational tool to aid high-throughput screening in catalysis

Abstract

Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of catalytic reactions on the surface of oxygen-covered and bare gold using machine learning. Using experimental data, we developed a machine learning model that maps reactants to products, using a chemical space representation. This involves predicting a chemical space value for the products, and then matching this value to a molecular structure chosen from a database. The database was developed by applying a set of possible reaction outcomes using known reaction mechanisms. Our machine learning approach 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.