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

Confined ion transport is involved in nanoporous ionicsystems.However, it is challenging to mechanistically predict its electricalcharacteristics for rational system design and performance evaluationusing an electrical circuit model due to the gap between the circuittheory and the underlying physical chemistry. Here, we demonstratethat machine learning can bridge this gap and produce physics-basednanocircuitry, based on equation discovery from the modified Poisson-Nernst-Plancksimulation results where an anomalous constructive diffusion-migrationinterplay of confined ions is unveiled. This bridging technique allowsus to gain physical insights into ion dynamics in nanoporous electrodes,such as nonideal cyclic voltammetry and dynamic, pore-size-dependentsurface condution.

Automated summary

Machine learning can be used to establish a physics-based nanocircuit model, allowing for the prediction and evaluation of electrical characteristics in nanoporous ionic systems. This approach provides insights into ion dynamics in nanoporous electrodes, such as nonideal cyclic voltammetry and dynamic, pore-size-dependent surface conductance.