Multiscale Investigation of Triboelectric Nanogenerator: An Expanded DFT Calculation Based on Machine Learning

Abstract Various types of triboelectric nanogenerators (TENGs) have been proposed, which rely on the details of electron transfer, ion migration, and mechanochemistry, but their largely different scales complicate the quantitative analysis of their contributions. In this study, we first investigate the electron transfer behavior at the nanoscale using density functional theory (DFT) calculations for the contact of two typical insulating materials, poly(vinyl chloride) (PVC) and poly(vinyl alcohol) (PVA), with an adsorbed water layer in between. The DFT results are then used to construct machine learning (ML) potentials, which enable us to execute larger-scale classical molecular dynamics (MD) simulations. The results demonstrate that electron transfer contributes to the generation of an electrostatic potential difference among materials, while ion migration compensates for part of the potential, leading to a reduced power output. This approach with ML establishes a multiscale simulation framework that links quantum-level mechanisms based on DFT to the investigation of mesoscale behaviors using classical MD simulations, providing insights into the interplay of charge transfer processes and offering guidance for the design of efficient TENG materials.