An abundance of high-quality data is key to enabling successful data-driven approaches for the exploration and optimisation of chemical systems using modern machine learning and artificial intelligence tools. Particularly for electrochemical energy applications – such as the discovery of materials for batteries, the characterisation of heterogeneous and homogeneous electrocatalysts, and the study of fundamental interfacial processes – there is a pressing need to explore vast ranges of chemical variables, such as solvent and electrolyte types, reactant concentrations, pH, and electrochemical parameters such as scan rates, electrodes, electrochemical windows and molecular motifs. To meet these needs, our group has recently introduced a range of tools under the umbrella of The Electrolab, an automated electrochemical platform that combines hardware, Python-based software, automated robotic systems for electrolyte dispensing, and custom-designed electrochemical chips, which together enable characterisation campaigns with minimal supervision. From simple but tedious experiments, such as determining diffusion coefficients, to systematic measurements of homogeneous electrocatalysis for alcohol oxidations, to complex surface characterisation experiments evaluating the redox capacity and dynamics of materials for redox-targeted flow batteries, our methods synergistically create new opportunities in electrochemistry. I will discuss prospects for a variety of materials and interface characterisation problems.
