Autonomous Discovery in the Chemical Sciences Part I: Progress
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 modeling. Part two reflects on these case studies and identifies a set of open challenges for the field.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 08, 2020
- Source ID
- 10.1002/anie.201909987
Entities
People
- Connor W. Coley
- Klavs F. Jensen
- Natalie S. Eyke
Organizations
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology