Experimental High‐Throughput Electrochemistry

Experimental high‐throughput electrochemistry (HTE) addresses fundamental limitations of classical electrochemical methods, which are often characterized by high manual effort, low experimental throughput, and limited reproducibility. By employing parallelized and automated experimental systems in combination with advanced data analysis techniques such as Bayesian optimization and machine learning, the development and optimization of electrochemical processes and materials can be significantly accelerated. Emphasis is placed on combinatorial approaches, automated laboratory platforms, and self‐driving systems. This review presents key technologies, application areas, and methodological advances in experimental HTE, including microelectrode arrays and robotics‐based platforms. The aim is to provide a comprehensive overview of the field, bridge existing knowledge gaps, contextualize current developments, and outline future innovation pathways for experimental electrochemical research. Although high‐throughput approaches have increasingly been applied across diverse areas such as battery research, electrocatalysis, and organic electrosynthesis, a coherent methodological overview of the underlying technologies, platform concepts, and levels of automation has been lacking. By consolidating previously scattered developments and systematically comparing different experimental strategies, this article provides a detailed picture of the current state of experimental HTE and identifies key directions for future research, particularly toward autonomous laboratory systems.