A Quantum Leap in Alloy Research: Machine Learning Speeds Up Diffusion Studies by 100x

University of Illinois researchers have developed a faster, more insightful method to model diffusion in alloys using “kinosons” and machine learning, potentially revolutionizing how this critical process is understood and studied. Credit: SciTechDaily.comand determine its diffusivity much more efficiently than by calculating entire trajectories.

A series of “states” connected with “transitions” in a complex system. Bigger dots correspond to states where more time is spent during simulation, thicker lines for faster transitions. To look at long trajectories with many jumps takes a lot of computational effort; the machine learning model converts this system to an equivalent one that has the same diffusivity behavior, but where calculation of diffusion is much simpler .

Another advantage of modeling diffusion using kinosons and machine learning is that it is significantly faster than calculating long-timescale, whole trajectories. Trinkle says that with this method, simulations can be done 100 times faster than it would take with the normal methods.

Source: Education Headlines (educationheadlines.net)

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