  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.
