On the classification and quantification of material defects in damaged crystalline solids by machine learned based MD simulations and a novel fingerprinting and visualization tool
2020.03.04 14:01 - Anna RędaszekIn this talk, I will discuss the analysis of damaged crystalline materials by presenting the computation of machine learning (ML) molecular dynamics (MD) interatomic potentials into the Gaussian Approximation Potential framework, to model efficiently the mechanism of energetic particle irradiation on crystalline solids. The obtained simulated damaged materials are then studied by a fingerprint-like method which is based on the calculation of a descriptor vector for each atom in the sample.
