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 …

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

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-
art approaches based on transition state modelling with density functional theory. Models …

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

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …

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 …

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 …

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 …

The need for more efficient catalytic processes is ever-growing, and so are the costs
associated with experimentally searching chemical space to find new promising catalysts …

Machine learning (ML) models can, once trained, make reaction barrier predictions in
seconds, which is orders of magnitude faster than quantum mechanical (QM) methods such …