Autonomous chemical science and engineering enabled by self-driving laboratories

Abstract

Recent advances in machine learning (ML) and artificial intelligence have provided an exciting opportunity to computerize the fundamental and applied studies of complex reaction systems via self-driving laboratories. Autonomous robotic experimentation can enable time-, material-, and resource-efficient exploration and/or optimization of high-dimensional space reaction systems. Furthermore, interpretation of the ML models trained on the experimental data can unveil the underlying reaction mechanisms. In this article, we discuss different elements of a self-driving lab, and present recent efforts in autonomous reaction modeling and optimization. Further development and adoption of ML-guided closed-loop experimentation strategies can realize the full potential of autonomous chemical science and engineering to accelerate the discovery and development of advanced materials and molecules. • An overview of the elements of self-driving laboratories. • Recent advancements in autonomous chemical science and engineering. • Recent progress in closed-loop autonomous reaction modeling and optimization. • Future opportunities in surrogate model-based reaction exploration.