Key requirements for advancing machine learning approaches in single entity electrochemistry

Abstract

Despite the noteworthy progress in Single Entity Electrochemistry (SEE) in the last decade, the field still must undergo further advancements to attain the requisite maturity for facilitating and propelling machine learning (ML)-based discoveries. This mini-review presents an analysis of the required developments in the domain, using the success of AlphaFold in biology as a benchmark for future progress. The first essential requirement is the creation and support of high-quality, centralized, and open-access databases on the electrochemical properties of single entities. This should be facilitated through the automation and standardization of experiments, promoting high-throughput output and facilitating comparison between datasets. Finally, the creation of a new type of interdisciplinary specialist, trained to pinpoint critical issues in SEE and implement solutions from applied informatics, is vital for ML approaches to flourish in the SEE field.