Machine Learning for Accelerated Defect Dynamics in Multicomponent Alloys

Understanding defect thermodynamics and transport is essential for predicting materials behavior at elevated temperatures. However, despite the exponential increase in computing power, the extreme disparity between atomistic, meso and macro scales prohibits direct brute-force simulations for most materials problems of practical interest. Going forward, realizing the full potential of multiscale modeling of increasingly complex materials through large-scale computing would require effective use of automation and artificial intelligence-based methods. Using defects transport in complex alloys as an example, this talk would provide an overview of the ongoing efforts at the Los Alamos National Laboratory that aim at addressing these challenges through the development of an integrated and automated multiscale simulation capability driven by exascale computing, rigorous uncertainty quantification, and machine learning.