Learning Uncertainty-Aware Models of Defect Kinetics at Scale - IPAM at UCLA

Explore cutting-edge techniques for modeling defect kinetics in materials at scale in this 53-minute lecture by Thomas Swinburne from the Centre National de la Recherche Scientifique. Delve into methods for rapidly exploring and coarse-graining energy landscapes of atomic systems with uncertainty quantification, focusing on thermally activated dynamics. Discover how descriptor techniques can be applied to capture a wide range of properties, including defect entropics and dislocation properties. Learn about scalable approaches to maximize diversity and mitigate costs in large-scale atomic data analysis. Examine the application of classical time series tools to descriptor trajectories for predicting complex systems like nanoparticle aging and dislocation network yielding with uncertainty awareness and extrapolation capabilities. Gain insights into connecting atomic-scale data to medium-dimensional metric spaces and overcoming challenges in modeling plasticity, thermodynamics, and kinetics of metals.

Thomas Swinburne - Learning uncertainty-aware models of defect kinetics at scale - IPAM at UCLA