Alladi Ramakrishnan Hall

Machine Learning prediction of glassy dynamics with particle size fluctuations

Misaki Ozawa

LIPhy, Grenoble

Amorphous materials, such as silica glasses, metallic glasses, colloids, foams, and granular materials, exhibit heterogeneous patterns of dynamics, often referred to as dynamical heterogeneity, particularly at lower temperatures or under external driving forces. Dynamical
heterogeneity consists of mobile and immobile spatial regions, suggesting the presence of non-trivial correlations. Recently, machine learning techniques have been applied to predict and forecast future dynamics, as well as the patterns of dynamical heterogeneity, using only static snapshots of the system. In this talk, I will introduce the basics of glassy dynamics and dynamical
heterogeneity, along with the relevant machine learning techniques. I will then discuss our recent work on predicting glassy dynamics, with a focus on fluctuations in particle sizes.