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Computational modeling demonstrates accurate macroscale diffusivity prediction in a complex multicomponent alloy, highlighting efficient kinetic mechanism quantification.
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)
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Chattopadhyay et al. (2024) studied this question.
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