Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

Over the last 20 to 25 years theoretical chemistry (particularly theoretical chemical kinetics)
has played an increasingly important role in develo** chemical kinetics models for …

The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the
framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and …

Computational chemistry provides a versatile toolbox for studying mechanistic details of
catalytic reactions and holds promise to deliver practical strategies to enable the rational in …

In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Computational methods based on quantum mechanical modeling are increasingly used to
provide insight into mechanistic aspects of homogeneous catalysis. While the potential and …

For the investigation of chemical reaction networks, the identification of all relevant
intermediates and elementary reactions is mandatory. Many algorithmic approaches exist …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subty** …

This article provides an overview on an automated reaction path search method called
artificial force induced reaction (AFIR). The AFIR method induces various chemical …