AI-driven electrocatalysis: reinforcement learning-based control and theoretical modeling of the glycerol oxidation reaction.

In this work, we present the full development process of a reinforcement-learning-based controller deployed in a closed-loop experimental setup to optimize the glycerol electrooxidation reaction. The conversion of glycerol to valuable products on platinum has attracted attention as a substitute for the oxygen evolution reaction in hydrogen-producing electrolyzers, motivated by the availability of glycerol as an inexpensive byproduct of biodiesel production. However, the glycerol oxidation reaction is known to poison platinum, requiring an intelligent operation procedure to sustain high conversion rates. The employed AI controller autonomously discovered a non-periodic pulsing strategy, which achieved on average a 30% higher charge transfer rate over the human-engineered pulsing protocol. In addition, we analyzed its decision-making using first-order Markov-chain modelling. This analysis revealed that the AI employs two distinct surface reactivation procedures: most of the time, it uses a short cycle, pulsing from the glycerol-oxidation potential to a relaxation potential of 0.3 V vs. RHE, while it periodically performs a longer cycle by pulsing first to 1.0 V vs. RHE and then to 0.3 V vs. RHE to revitalize the system. To further support these findings, we developed an interpretable physics-inspired AI model that simulates surface dynamics during glycerol oxidation and captures the evolving distribution of active and poisoned sites. Although demonstrated for glycerol oxidation, both the AI-control framework and the physics-inspired AI model are broadly applicable to other electrochemical systems, offering a general pathway for integrating AI methods into experimental electrocatalytic research.