Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

The estimation of chemical reaction properties such as activation energies, rates, or yields is
a central topic of computational chemistry. In contrast to molecular properties, where …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

Single‐molecule localization microscopy (SMLM) has found extensive applications in
various fields of biology and chemistry. As a vital component of SMLM, fluorophores play an …

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

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 …

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 …

The interplay of kinetics and thermodynamics governs reactive processes, and their control
is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium …

Abstract Machine learning (ML) approaches have enabled rapid and efficient molecular
property predictions as well as the design of new novel materials. In addition to great …