Integrating Machine Learning with Triboelectric Nanogenerators: Optimizing Electrode Materials and Doping Strategies for Intelligent Energy Harves

TL;DR

This paper presents a machine learning framework using graph neural networks to optimize electrode materials and doping strategies in triboelectric nanogenerators, significantly improving energy density and reducing experimental costs.

Contribution

It introduces a novel data-driven approach combining experimental data with machine learning to predict and enhance TENG performance, advancing intelligent material design.

Findings

65.7% increase in energy density for aluminum-doped PTFE

85.7% improvement for fluorine-doped PTFE

PTFE with 7% silver doping achieves 1.12 J/cm$^2$ energy density

Abstract

The integration of machine learning techniques with triboelectric nanogenerators (TENGs) offers a transformative pathway for optimizing energy harvesting technologies. In this study, we propose a comprehensive framework that utilizes graph neural networks to predict and enhance the performance of TENG electrode materials and doping strategies. By leveraging an extensive dataset of experimental and computational results, the model effectively classifies electrode materials, predicts optimal doping ratios, and establishes robust structure-property relationships. Key findings include a 65.7% increase in energy density for aluminum-doped PTFE and an 85.7% improvement for fluorine-doped PTFE, highlighting the critical influence of doping materials and their concentrations. The model further identifies PTFE as a highly effective negative electrode material, achieving a maximum energy density of 1.12 J/cm$^2$ with 7% silver (Ag) doping when copper (Cu) is used as the positive electrode. This data-driven approach not only accelerates material discovery but also significantly reduces experimental costs, providing novel insights into the fundamental factors influencing TENG performance. The proposed methodology establishes a robust platform for intelligent material design, advancing the development of sustainable energy technologies and self-powered systems.