Physics-Based Machine Learning Discovered Nanocircuitry for Nonlinear Ion Transport in Nanoporous Electrodes

Confined ion transport is involved in nanoporous ionic systems. However, it is challenging to mechanistically predict its electrical characteristics for rational system design and performance evaluation using an electrical circuit model due to the gap between the circuit theory and the underlying physical chemistry. Here, we demonstrate that machine learning can bridge this gap and produce physics-based nanocircuitry, based on equation discovery from the modified Poisson-Nernst-Planck simulation results where an anomalous constructive diffusion-migration interplay of confined ions is unveiled. This bridging technique allows us to gain physical insights into ion dynamics in nanoporous electrodes, such as nonideal cyclic voltammetry and dynamic, pore-size-dependent surface condution.