(Submitted on 30 Mar 2020)

Abstract: 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 discoveries of physical matter (molecules, materials,
devices), processes, and models and how they are unified as search problems. We
then introduce a set of questions and considerations relevant to assessing the
extent of autonomy. Finally, we describe many case studies of discoveries
accelerated by or resulting from computer assistance and automation from the
domains of synthetic chemistry, drug discovery, inorganic chemistry, and
materials science. These illustrate how rapid advancements in hardware
automation and machine learning continue to transform the nature of
experimentation and modelling.

Part two reflects on these case studies and identifies a set of open
challenges for the field.