Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …

Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key
role in areas of cheminformatics and drug discovery. Existing generative models have …

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 …

Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms
and predicting reaction outcomes. However, the lack of experimental data and the steep …

Quantitative chemical reaction data, including activation energies and reaction rates, are
crucial for developing detailed kinetic mechanisms and accurately predicting reaction …

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 …

Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to
produce a diverse array of advanced biofuels with complex molecular structure and …

The elucidation of transition state (TS) structures is essential for understanding the
mechanisms of chemical reactions and exploring reaction networks. Despite significant …