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 …

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

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 …

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 …

Being able to predict the course of arbitrary chemical reactions is essential to the theory and
applications of organic chemistry. Previous approaches are not high-throughput, are not …

Physics-based representations constructed using only atomic positions and nuclear charges
(also known as quantum machine learning, QML) allow for the reliable and efficient …

There is a perceived dichotomy between structure-based and descriptor-based molecular
representations used for predictive chemistry tasks. Here, we study the performance …

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 …

Studying organic reaction mechanisms using quantum chemical methods requires from the
researcher an extensive knowledge of both organic chemistry and first-principles …

Proposing reasonable mechanisms and predicting the course of chemical reactions is
important to the practice of organic chemistry. Approaches to reaction prediction have …