2017 journal article

Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage

JOURNAL OF APPLIED STATISTICS, 44(10), 1761–1784.

By: G. Peterson n, D. Li n, B. Reich n  & D. Brenner n

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: Materials science; uncertainty quantification; computer emulation; MCMC; multivariate
Source: Web Of Science
Added: August 6, 2018

Molecular dynamic computer simulation is an essential tool in materials science to study atomic properties of materials in extreme environments and guide development of new materials. We propose a statistical analysis to emulate simulation output with the ultimate goal of efficiently approximating the computationally intensive simulation. We compare several spatial regression approaches including conditional autoregression (CAR), discrete wavelets transform (DWT), and principle components analysis (PCA). The methods are applied to simulation of copper atoms with twin wall and dislocation loop defects, under varying tilt tension angles. We find that CAR and DWT yield accurate results but fail to capture extreme defects, yet PCA better captures defect structure.