Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes

TL;DR

This paper introduces a machine learning approach that uncovers physics-based nano-circuitry models from ion transport simulations, providing new insights into ion dynamics in nanoporous electrodes for improved system understanding.

Contribution

The study presents a novel machine learning method that discovers nano-circuitry models directly from physical simulations, bridging the gap between circuit theory and physical chemistry.

Findings

Unveiled an anomalous diffusion-migration interplay of confined ions.

Produced physics-based nano-circuitry models from simulation data.

Gained insights into ion dynamics and non-ideal cyclic voltammetry.

Abstract

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 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 nano-circuitry, 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 of ion dynamics in nanoporous electrodes, such as the non-ideal cyclic voltammetry.