Simulating diffusion using 'kinosons' and machine learning

Researchers have recast diffusion in multicomponent alloys as a sum of individual contributions, called 'kinosons.' Using machine learning to compute the statistical distribution of the individual contributions, they were able to model the alloy and calculate its diffusivity orders of magnitude more efficiently than computing whole trajectories.

Here, the team modeled diffusion in multicomponent alloys, which are metals composed of five different elements -- manganese, cobalt, chromium, iron and nickel in this research -- in equal amounts. These types of alloys are interesting because one way to make strong materials is to add different elements together like adding carbon and iron to make steel.

Therefore, any jump that an atom makes contributes to diffusion and the problem becomes a lot easier to solve."We call those jumps kinosons, for little moves," Trinkle says."We've shown that you can extract the distribution of those, the probability of seeing a kinoson of a certain magnitude, and add them all up to get the true diffusivity. On top of that you can tell how different elements are diffusing in a solid.

Education Education Latest News, Education Education Headlines

Similar News:You can also read news stories similar to this one that we have collected from other news sources.

University of Alaska-Fairbanks researchers using drones to measure sea iceSea ice is vital in Alaska. It's used as a hunting platform, protects coastal communities from winter storm and is considered a good measure of a changing climate. UAF Geophysical Institute researchers are testing a drone-mounted tool to measure the depth of sea ice.
Source: AKNewsNow - 🏆 460. / 53 Read more »