Contributions to Diffusion in Complex Materials Quantified with Machine Learning

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.Received 11 January 2024Revised 17 March 2024Accepted 5 April 2024DOI:https://doi.org/10.1103/PhysRevLett.132.186301© 2024 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDiffusionTechniquesMachine learningMaterials modelingMonte Carlo methodsCondensed Matter, Materials & Applied Physics