Interpretable Machine Learning for Evaluating Nanogenerators’ Structural Design

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.