Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance

Abstract

This study employs various machine learning algorithms to model the electrical conductivity and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a comprehensive dataset gathered from over 100 references. Artificial neural networks (ANNs) demonstrated superior predictive accuracy among the models. The investigation delves into identifying and mitigating outliers, both structural and response-related, showcasing the robustness of the proposed ANN models. The study emphasizes the critical role of applicability domain (AD) analysis in evaluating model reliability. Results indicate high accuracy for electrical conductivity (RMSE: 0.408, R2: 0.984) and gas sensing responses for ammonia, toluene, and benzene gases (RMSE: 0.350, 0.232, and 0.081, R2: 0.967, 0.983, and 0.976, respectively). Input contribution analysis highlights key parameters influencing performance. The sigma-profiles of additives emerge as significant contributors, emphasizing the importance of molecular-input understanding in machine learning models. These findings contribute to developing high-performance PANI/Gr nanocomposites with implications for diverse applications like supercapacitors, gas sensors, and energy storage devices. The study underscores the need for further research to deepen the understanding of molecular inputs' impact on PANI/Gr system performance, enabling more precise material design.