@article{li_bucholz_peterson_reich_russ_brenner_2017, title={How predictable is plastic damage at the atomic scale?}, volume={110}, number={9}, journal={Applied Physics Letters}, author={Li, D. and Bucholz, E. W. and Peterson, G. and Reich, B. J. and Russ, J. C. and Brenner, D. W.}, year={2017} }
 @article{li_reich_brenner_2017, title={Statistical and image analysis for characterizing simulated atomic-scale damage in crystals}, volume={135}, ISSN={["1879-0801"]}, DOI={10.1016/j.commatsci.2017.03.054}, abstractNote={While molecular dynamics simulations have been used for decades to study structure and formation mechanisms of plastic damage in crystals, the analytical tools needed to characterize collections of plastic defects have been limited. Here we demonstrate the use of two methods, spatial cross-correlations (CC) and Linear Discriminate Analysis (LDA), to analyze and compare plastic damage profiles among molecular dynamics simulations in which damage was created by straining bi-crystals containing symmetric tilt grain boundaries with different tilt angles. Two potentials were used, one representing Cu and one representing Ag, and two coarse-grained descriptors for different types of crystal damage were used, averaged central symmetry parameters (CSP) and atomic hydrostatic stress (HS). We find that in general the CSP is a more accurate descriptor than HS for both analysis methods, and for data base sizes of about 30 or more simulations per tilt angle, the LDA does considerably better in predicting angle and material than the CC method. For example, at the largest data base size of 50 simulations per tilt angle and using the average CSP values, the LDA predicts the exact initial tilt angle and material type for 92% of the simulations, while the CC approach drops to 58%. If the average HS is used instead of the average CSP, the LDA and CC predictions drop to 63% and 32%, respectively. These results point to a number of possible applications of this method, for example in quantifying how the range of damage for a set of strained systems may depend on strain rate or temperature, or quantifying similarities between complex damage from processes such as indentation and energetic ion bombardment.}, journal={COMPUTATIONAL MATERIALS SCIENCE}, author={Li, D. and Reich, B. J. and Brenner, D. W.}, year={2017}, month={Jul}, pages={119–126} }
 @article{li_reich_brenner_2017, title={Using spatial cross-correlation image analysis to characterize the influence of strain rate on plastic damage in molecular dynamics simulations}, volume={25}, number={7}, journal={Modelling and Simulation in Materials Science and Engineering}, author={Li, D. and Reich, B. J. and Brenner, D. W.}, year={2017} }
 @article{peterson_li_reich_brenner_2017, title={Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage}, volume={44}, ISSN={["1360-0532"]}, DOI={10.1080/02664763.2016.1221915}, abstractNote={ABSTRACT 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.}, number={10}, journal={JOURNAL OF APPLIED STATISTICS}, author={Peterson, Geoffrey Colin L. and Li, Dong and Reich, Brian J. and Brenner, Donald}, year={2017}, pages={1761–1784} }