Interpretable Machine Learning for Evaluating Nanogenerators’ Structural Design

Abstract

The limited battery life in modern mobile, wearable, and implantable electronics critically constrains their operational longevity and continuous use. Consequently, as a self-powered technology, triboelectric nanogenerators (TENGs) have emerged as a promising solution to this. Traditional approaches for evaluating TENG structural design typically require manual, repetitive, time-consuming, and high-cost finite element modeling or experiments. To overcome this bottleneck, we developed a fully automated platform that leverages machine learning (ML) techniques. Our framework contains an artificial neuron network-based surrogate model that can provide accurate and reliable performance predictions for any structural parameters and a TreeSHAP interpretable ML model that can generate precise global and local insights for TENG structural parameters. Our platform shows broad adaptability to multiple TENG structures. In summary, our platform is an integrated platform that utilizes interpretable ML techniques to solve the complex multidimensional TENG structural evaluation problem, marking a significant advancement in TENG design and supporting sustainable energy solutions in mobile electronics.