Predicting Voltammetry Using Physics-Informed Neural Networks.

ABSTRACT: We propose a discretization-free approach to simulation of cyclic voltammetry using Physics-Informed Neural Networks (PINNs) by constraining a feed-forward neutral network with the diffusion equation and electrochemically consistent boundary conditions. Using PINNs, we first predict one-dimensional voltammetry at a disc electrode with semi-infinite or thin layer boundary conditions. The voltammograms agree quantitatively with those obtained independently using the finite difference method and/or previously reported analytical expressions. Further, we predict the voltammetry at a microband electrode, solving the two-dimensional diffusion equation, obtaining results in close agreement with the literature. Last, we apply a PINN to voltammetry at the edges of a square electrode, quantifying th

SUBMITTER: Chen H 

PROVIDER: S-EPMC9084599 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature