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We used equilibrium and non-equilibrium atomistic simulations to probe the influence of anion chemistry on the true conductivity, dynamical correlations, and ion transport mechanisms in polymeric ionic liquids. An inverse correlation was found between anion self-diffusivities, ionic mobilities, and the anion size for spherical anions. While some larger asymmetric anions had higher diffusivities than smaller spherical anions, their diffusivities and mobilities did not exhibit a direct correlation to the anion volumes. The conductivity and anion dynamical correlations also followed the same trends as displayed by the diffusivity and mobility of anions. All the systems we examined displayed positively correlated motion among anions, suggesting a contribution that enhances the conductivity beyond the ideal Nernst–Einstein value. Analysis of ion transport mechanisms demonstrated very similar hopping characteristics among the spherical anions despite differences in their sizes.

 

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. 

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.