Simulating diffusion using 'kinosons' and machine learning (IMAGE)
Caption
A series of "states" (dots) connected with "transitions" (lines) 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 (left) to an equivalent one that has the same diffusivity behavior, but where calculation of diffusion is much simpler (right). In the uncorrelated system, each jump corresponds to a "kinoson," a small contribution to diffusion and the sum of all kinosons gives the diffusivity.
Credit
The Grainger College of Engineering at the University of Illinois Urbana-Champaign
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Original content