  The energy-delivery performance of supercapacitors is fundamentally
determined by the dynamics of ions confined in nanoporous materials, which has
attracted intensive interest in nanoscopic research. Many nanoscopic
understandings, including changes in in-pore ion concentration and mobility
during dynamical processes, are continuously reported. However, quantitative
scale-up of these nanoscopic understandings for evaluation of the macroscopic
performance of supercapacitors is difficult due to the absence of links between
these scales. Here we demonstrate that machine learning can be used to
establish such links. Starting from nanoscale, we first reveal a
diffusion-enhanced migration of ions in nanopores using primarily modified
Poisson-Nernst-Planck model, unlike in bulk electrolyte where diffusion
counteracts migration. Using machine learning, we discover a dynamically
varying ionic resistance and its equation, resulting from the in-pore ion
concentration change contributed by diffusion-enhanced migration. The obtained
equation is used to construct a nano-circuitry model (NCM), which describes
both the macroscopic performance of supercapacitors and nanometre-resolved
ionic behaviour. We demonstrate that NCM can provide additional perspectives to
understand cyclic voltammograms. A Faradaic-like current peak can show in
non-Faradaic processes, and an asymmetric charging/discharging can occur
without ion desolvation. These is because the dynamically varying resistance
delivers ions effectively for storage. The demonstrated use of machine learning
could extend to other ionic systems including batteries and desalination,
paving the route towards rational design.

Mesoscale and Nanoscale Physics;Applied Physics;Chemical Physics;Computational Physics;Fluid Dynamics