Autonomous discovery in the chemical sciences part I: Progress
It provides a framework for assessing autonomy in chemical discovery, relevant for researchers in chemistry and materials science, but is incremental as a review.
This review classifies discoveries in chemical sciences as search problems and surveys case studies where automation and machine learning have accelerated experimentation and modeling in areas like synthetic chemistry and materials science.
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.