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

Predicting the rate constants of elementary reaction steps is key for the computational
modeling of catalytic processes. Within transition state theory (TST), this requires an …

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

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 …

Many important industrial processes rely on heterogeneous catalytic systems. However,
given all possible catalysts and conditions of interest, it is impractical to optimize most …

Based on a series of energy minimizations with starting structures obtained from the Baker
test set of 30 organic molecules, a comparison is made between various open-source …

Ab-initio methods such as density functional theory (DFT) is useful for fundamental atomistic-
level study and is widely used across many scientific fields, including for the discovery of …

Access to the potential energy Hessian enables determination of the Gibbs free energy and
certain approaches to transition state search and optimization. Here, we demonstrate that off …

We combine automatic process exploration with an iteratively trained machine-learning
interatomic potential to systematically identify elementary processes occurring during the …

In this work, three versions of self-consistent field/Kohn–Sham density functional theory
(SCF/KS-DFT) orbital optimization are described and benchmarked. The methods are a …