Automation of rate-coefficient calculations for gas-phase organic species became possible
in recent years and has transformed how we explore these complicated systems …

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

Specialized computational chemistry packages have permanently reshaped the landscape
of chemical and materials science by providing tools to support and guide experimental …

The study presents an ab‐initio based framework for the automated construction of
microkinetic mechanisms considering correlated uncertainties in all energetic parameters …

Identifying transition states—saddle points on the potential energy surface connecting
reactant and product minima—is central to predicting kinetic barriers and understanding …

Direct access to transition state energies at low computational cost unlocks the possibility of
accelerating catalyst discovery. We show that the top performing graph neural network …

Ammonia, which can be decomposed on-site to produce CO2-free H2, is regarded as a
promising hydrogen carrier because of its high hydrogen density, wide availability, and ease …

Driven by synergic advancements in high performance computing and theory, the capability
to estimate rate constants from first principles has evolved considerably recently. When this …

We assess the capability of machine-learned potentials to compute rate coefficients by
training a neural network (NN) model and applying it to describe the chemical landscape on …

We present a new algorithm for the optimization of molecular structures to saddle points on
the potential energy surface using a redundant internal coordinate system. This algorithm …