Opportunities and challenges in applying machine learning to voltammetric mechanistic studies

Voltammetry is a powerful tool for providing quantitative mechanistic information associated with a broad range of chemically or biologically important electron transfer processes. An important step in voltammetric data analysis is to compare experimental data with those derived by simulations based on a mechanism chosen by the experimenter to determine the 'best fit', which can be achieved either heuristically or by a computationally supported automated method. In recent years, machine learning methods have emerged as a powerful tool in mechanism classification and parametrisation, owing to the rapid increase in computing power and widespread accessibility of machine learning platforms. This opinion article gives an overview of the historical development and current status of machine learning in this field, highlights the opportunities and challenges, and predicts possible future directions.

Automated summary

Voltammetry is a powerful tool for obtaining quantitative mechanistic information in important electron transfer processes. Machine learning methods have emerged as a powerful tool in voltammetry data analysis, offering opportunities for rapid identification of the best fitting mechanism through data comparison and automated methods.