Contributions to Diffusion in Complex Materials Quantified with Machine Learning

Soham Chattopadhyay, Dallas R. Trinkle

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number186301
JournalPhysical review letters
Issue number18
StatePublished - May 3 2024

ASJC Scopus subject areas

  • General Physics and Astronomy


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