Diffusion in a d-dimensional rough potential.

Roughness in a noisy solid solution in d dimensions always decreases diffusivity, and any diffusivity increase from alloying elements must come from lower average activation energy rather than noise.

• Why it matters: Predicting diffusion in solids is needed to model microstructure evolution out of equilibrium, but atomistic approaches are often too computationally expensive.
• What they did: The authors build an analytical diffusivity model in arbitrary d using mean first passage time analysis, then validate it by comparing with kinetic Monte Carlo simulations.
• The result: The analytical predictions match kinetic Monte Carlo quantitatively for d ≤ 5 and show excellent quantitative agreement for d ≤ 3, supporting the model’s dimensional generalization.

AI summary of the abstract · may contain errors · not medical advice · read the paper.