Mechanistic understanding of organic reactions can facilitate reaction development, impurity
prediction, and in principle, reaction discovery. While several machine learning models have …

Mechanistic understanding of organic reactions can facilitate reaction development, impurity
prediction, and in principle, reaction discovery. While several machine learning models have …

A mechanistic understanding of catalytic organic reactions is crucial for the design of new
catalysts, modes of reactivity and the development of greener and more sustainable …

Understanding organic reaction mechanisms is crucial for interpreting the formation of
products at the atomic and electronic level, but still remains as a domain of knowledgeable …

As more data are introduced in the building of models of chemical reactivity, the mechanistic
component can be reduced until 'big data'applications are reached. These methods no …

Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …

Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in
rational reactivity design. However, model training requires large data sets of typically …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Approaches to the reaction prediction problems can be …

Quantitative predictions of reaction properties, such as activation energy, have been limited
due to a lack of available training data. Such predictions would be useful for computer …