Machine learning in fundamental electrochemistry: Recent advances and future opportunities
Haotian Chen, Enno Kätelhön, Richard G. Compton
Abstract
The last decade has seen a rapid increase in the use of machine learning techniques in an ever-broadening range of applications. Despite great opportunities, its benefits have, however, not yet been exploited to the full extent in the field of electrochemistry. This paper briefly reviews recent activities at the interface of machine learning and electrochemistry, discusses the challenges researchers have encountered, and points out opportunities for future research and application.
Topics & Concepts
NanotechnologyComputer scienceField (mathematics)Data scienceEngineering ethicsEngineeringMaterials scienceMathematicsPure mathematicsElectrochemical Analysis and ApplicationsAdvanced Chemical Sensor TechnologiesMachine Learning in Materials Science