Module 3

Machine Learning for Predicting Dynamical Heterogeneities in Materials

(Gaurav Arya, Jianfeng Lu)

In this module, students will investigate glassy liquids through a combination of molecular dynamics (MD) simulations and ML classification methods to identify and develop structural predictors for dynamic heterogeneities in glassy systems. Students will learn the theory behind MD simulations and glassy materials, get hands-on training in performing MD simulations of glassy liquids, and learn how to calculate structural and dynamical properties from simulation data. The students will also learn several ML classification methods, including support vector machines, logistic regression, and deep learning, and apply these methods to identify local structural characteristics that lead to dynamical heterogeneities in glassy liquids. We will also discuss how to apply ML techniques like clustering and wavelets to analyze local defects in MD or experimental images for poly-crystalline materials. 

Learning objectives: 

1. Students will learn the fundamentals of molecular modeling and simulations and ML-based classification methods

2. Students will be able to carry out MD simulations and compute material properties from simulation data.

3. Students will be able to apply ML classification techniques to build structure-function relationships in materials.