This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …

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

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 …

The area of reaction mechanism discovery simulation has taken considerable strides in
recent years. Novel methods that make hypotheses for elementary steps and …

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

The chemical modeling of combustion treats the chemical conversion of hundreds of species
through thousands of reactions. Recent advances in theoretical methodologies and …

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

Quantitative predictions of reaction properties, such as activation energy, have been limited
due to a lack of available training data. Such predictions would be useful for computer …

Calculating reaction energy profiles to aid in mechanistic elucidation has long been the
domain of the expert computational chemist. Here, we introduce autodE (https://github …