Abstract

The integration of machine learning (ML) with electrochemical analysis represents a significant advancement in the field of analytical chemistry, facilitating improved data interpretation and decision-making processes. This article explores the methodologies employed in combining ML techniques with various electrochemical analysis methods, such as voltammetry, impedance spectroscopy, and potentiometry. It discusses the benefits of this integration, including enhanced accuracy, efficiency, and predictive capabilities. Furthermore, we examine the challenges associated with the implementation of ML in electrochemical analysis, such as data quality and algorithm selection. Through case studies and practical applications, this article highlights how ML can transform electrochemical analysis and pave the way for innovative solutions in fields such as environmental monitoring, pharmaceuticals, and energy storage. The conclusion emphasizes the future potential of this integration and its implications for advancing electrochemical research.