posted on 2021-04-29, 19:33authored bySanket Kadulkar, Michael P. Howard, Thomas M. Truskett, Venkat Ganesan
We
develop a convolutional neural network (CNN) model to predict
the diffusivity of cations in nanoparticle-based electrolytes and
use it to identify the characteristics of morphologies that exhibit
optimal transport properties. The ground truth data are obtained from
kinetic Monte Carlo (kMC) simulations of cation transport parametrized
using a multiscale modeling strategy. We implement deep learning approaches
to quantitatively link the diffusivity of cations to the spatial arrangement
of the nanoparticles. We then integrate the trained CNN model with
a topology optimization algorithm for accelerated discovery of nanoparticle
morphologies that exhibit optimal cation diffusivities at a specified
nanoparticle loading, and we investigate the ability of the CNN model
to quantitatively account for the influence of interparticle spatial
correlations on cation diffusivity. Finally, by using data-driven
approaches, we explore how simple descriptors of nanoparticle morphology
correlate with cation diffusivity, thus providing a physical rationale
for the observed optimal microstructures. The results of this study
highlight the capability of CNNs to serve as surrogate models for
structure–property relationships in composites with monodisperse
spherical particles, which can in turn be used with inverse methods
to discover morphologies that produce optimal target properties.