Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis

Abstract

Self-driving laboratories (SDLs) represent a cutting-edge convergence of machine learning with laboratory automation. SDLs operate in active learning loops, in which a machine learning algorithm plans experiments that are subsequently executed by increasingly automated (robotic) modules. Here we present our view on emerging SDLs for accelerated discovery and process optimization in heterogeneous catalysis. We argue against the paradigm of full automation and the goal of keeping the human out of the loop. Based on analysis of the involved workflows, we instead conclude that crucial advances will come from establishing fast proxy experiments and re-engineering existing apparatuses and measurement protocols. Industrially relevant use cases will also require humans to be kept in the loop for continuous decision-making. In turn, active learning algorithms will have to be advanced that can flexibly deal with corresponding adaptations of the design space and varying information content and noise in the acquired data. Uses of machine learning and automation are increasing and these techniques are becoming popular in catalysis research. This Perspective discusses how active learning workflows and human intervention should be optimized to ensure the most efficient progress for emerging self-driving laboratories performing heterogeneous catalysis research.