Enabling Reliable Data-Driven Tafel Analysis

The Tafel slope is well known as a critical parameter for characterizing a wide spectrum of electrocatalysis, serving as a key descriptor not only of their performance but also of features of the microscopic mechanism. While current-voltage measurements are an essential tool in the evaluation of electrocatalysts’ performances, the arbitrary selection of the Tafel regime and the lack of rigorous statistical methods to justify this choice undermine the objectivity and reproducibility of the derived Tafel values. As a result, this lack of standardization hinders the development of a universal protocol for calculating Tafel analysis and risks introducing inconsistencies across different research groups. Herein, we show a simple and robust Bayesian optimization method to identify the optimal Tafel regime from any given dataset. Our approach remains effective even when multiple Tafel regions are present in the original data. The code for performing this analysis is available as open-source software, facilitating broader adoption and application. Its performance is tested against 379 experimental current-voltage measurements reported by different groups. By enabling more reliable interpretation of electrochemical data, this work contributes to advancing the data-driven development of future green energy technologies.