Dataset for Explainable Operating-Regime Analysis of Piezoelectric Energy Harvesters

Dataset and Analysis Results This repository contains the curated dataset and numerical analysis results associated with the manuscript entitled: “Explainable Machine Learning for Operating-Regime Discovery and Voltage-Response Mapping in PVDF/BaTiO₃/MWCNT Piezoelectric Energy Harvesters” The study investigates the voltage-response behavior of PVDF/BaTiO₃/MWCNT piezoelectric energy harvesters under different composition, applied-force, and excitation-frequency conditions. The deposited files include the curated dataset used in the analyses and the numerical outputs supporting the results reported in the manuscript. The analyses include condition-level statistical evaluation, local response–sensitivity mapping, data-driven operating-regime identification, feature-group analysis, and post-hoc explainability analysis. Users of these data should cite the associated article and this dataset record when using the deposited data or results in subsequent research. Associated article: [Full citation to be added after publication] Dataset DOI: 10.5281/zenodo.22018818