This code is to show how the machine learning method is used to combinning the kMC in the paper "Revealing-the-crucial-role-of-rough-energy-landscape-on-self-diffusion-in-high-entropy-alloys" A CST method was firstly used to combining the ML model with the kMC, which make the ML model can predict the on-the-fly barrier energies with almost the same accuracy as the static model for the dynamic kMC modleling.
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Most of the outstanding functional and structural performance in high-entropy alloys (HEAs) relates to their sluggish diffusion properties under the rough potential energy landscape (PEL) induced by intrinsic chemical disorder. Due to the highly rugged and multi-dimensional nature of PEL, it is challenging to describe how the diffusion process i…
Jeremy1189/Revealing-the-crucial-role-of-rough-energy-landscape-on-self-diffusion-in-high-entropy-alloys
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Most of the outstanding functional and structural performance in high-entropy alloys (HEAs) relates to their sluggish diffusion properties under the rough potential energy landscape (PEL) induced by intrinsic chemical disorder. Due to the highly rugged and multi-dimensional nature of PEL, it is challenging to describe how the diffusion process i…
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