TY - JOUR
T1 - Contributions to Diffusion in Complex Materials Quantified with Machine Learning
AU - Chattopadhyay, Soham
AU - Trinkle, Dallas R.
N1 - The authors thank Dr. Danny Perez and Professor Lee DeVille for helpful conversations, and Professor Sergiy Divinski for helpful conversations and the experimental data in Fig. . This work is sponsored by the NSF under Program No. MPS-1940303. This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA), and which is supported by funds from the University of Illinois at Urbana-Champaign. The code is available at , and the data are available at .
PY - 2024/5/3
Y1 - 2024/5/3
N2 - 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.
AB - 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.
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U2 - 10.1103/PhysRevLett.132.186301
DO - 10.1103/PhysRevLett.132.186301
M3 - Article
C2 - 38759179
AN - SCOPUS:85192063822
SN - 0031-9007
VL - 132
JO - Physical review letters
JF - Physical review letters
IS - 18
M1 - 186301
ER -