Application of XGBoost using data from the oxygen reduction reaction of carbonaceous materials for the electrosynthesis of H2O2

This dataset is associated with the article entitled “Application of XGBoost using data from the oxygen reduction reaction of carbonaceous materials for the electrosynthesis of H2O2” and comprises a curated database constructed from experimental data reported in the literature on the cathodic electrosynthesis of hydrogen peroxide (H₂O₂) using carbon-based materials. The database includes experimental operating conditions, physicochemical properties, catalyst characterization parameters, and performance metrics related to H₂O₂ production. Specifically, the dataset covers information derived from X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, Brunauer–Emmett–Teller (BET) surface area analysis, contact angle measurements, and electrical property analyses, as well as electrochemical performance indicators. The data were compiled, organized, and processed to serve as input for the machine learning analyses presented in the associated article, where an XGBoost-based model was developed to evaluate the influence of material properties and experimental conditions on H₂O₂ electrosynthesis efficiency. The dataset was also used for model validation, hyperparameter optimization, and feature relevance analysis. This dataset is provided to support transparency, reproducibility, and further data-driven research in the field of H₂O₂ electrosynthesis and machine learning-assisted catalyst design.