Contributions to Diffusion in Complex Materials Quantified with Machine Learning.

Abstract

Using machine learning with a variational formula for diffusivity, we recast diffusion as a sum of individual contributions to diffusion-called "kinosons"-and compute their statistical distribution to model a complex multicomponent alloy. Calculating kinosons is orders of magnitude more efficient than computing whole trajectories, and it elucidates kinetic mechanisms for diffusion. The density of kinosons with temperature leads to new accurate analytic models for macroscale diffusivity. This combination of machine learning with diffusion theory promises insight into other complex materials.