Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism’s presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Here we provide the source data and source code associated with our manuscript (NCOMMS-23-50331-T) entitled "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation" for its forthcoming publication in Nature Communications. 'Source Data.zip' contains the raw data for the main text figures. 'Source Code.zip' contains both the code for automated exhaustive experiment and the code for autonomous closed-loop workflow presented in the manuscript. 'Deep-learning model for voltammogram analysis.zip' contains the deep-learning model file used in the code.