Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Abstract

The urgency with which fundamental questions of energy conversion and the sustainable use of raw materials must be solved today requires new approaches in catalysis research. One way is to couple high-throughput experiments with machine learning methods in autonomous catalyst development. The fact that the active form of a catalyst is only created under working conditions and that the catalytic function is always in a very complex relationship with a number of physical and chemical properties of the material makes it essential to integrate operando experiments into systems of autonomous catalyst development. The analysis of the current state of the art and knowledge revealed a lack of integration of the numerous, technically very different unit operations in catalyst discovery and a great need for new developments in online and in situ analytics, especially in catalyst synthesis. To pave the way for autonomous processing of work packages by robots, it is proposed to advance the automation of single unit operations currently performed by human researchers by introducing standard operating procedures described in handbooks. The work according to rigorous protocols produces, on the one hand, reliable data that can be evaluated using artificial intelligence and facilitates on the other hand the automation. Special attention should be paid to the acquisition and real-time evaluation of analytical data in in situ and operando experiments as well as the automatic storage of data and metadata in databases.