Predicting chemical activation energies is one of the longstanding and important challenges
in computational chemistry. Recent advances have shown that machine learning can be …

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 …

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way
synergy to build chemical reaction expert systems. The proposed ML approach identifies …

Application of machine learning (ML) to the prediction of reaction activation barriers is a new
and exciting field for these algorithms. The works covered here are specifically those in …

Machine learning has been used to study chemical reactivity for a long time in fields such as
physical organic chemistry, chemometrics and cheminformatics. Recent advances in …

Quantum and classical reaction rate constant calculations come at the cost of exploring
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …

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 …

Artificial intelligence is driving one of the most important revolutions in organic chemistry.
Multiple platforms, including tools for reaction prediction and synthesis planning based on …

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 …

Kinetic modeling can predict the performance of a reaction system and aids in
understanding detailed reaction chemistry. However, high-fidelity reaction simulations …