  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 elucidates kinetic mechanisms
for diffusion. The distribution 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.
