Dynamic Classification of Defect Structures in Molecular Dynamics Simulation Data

In this application paper we explore techniques to classify anomalous structures (defects) in data generated from Molecular Dynamics (MD) simulations of Silicon (Si) atom systems. These systems are studied to understand the processes behind the formation of various defects as they have a profound impact on the electrical and mechanical properties of Silicon. In our prior work [12, 13, 14] we presented techniques for defect detection. Here, we present a two-step dynamic classifier to classify the defects. The first step uses up to third-order shape moments to provide a smaller set of candidate defect classes. The second step assigns the correct class to the defect structure by considering the actual spatial positions of the individual atoms. The dynamic classifier is robust and scalable in the size of the atom systems. Each phase is immune to noise, which is characterized after a study of the simulation data. We also validate the proposed solutions by using a physical model and properties of lattices. We demonstrate the efficacy and correctness of our approach on several large datasets. Our approach is able to recognize previously seen defects and also identify new defects in real time.