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Kalidindi SR, Gomberg JA, Trautt ZT, et al. Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets. Nanotechnology. 2015;26(34):344006doi: 10.1088/0957-4484/26/34/344006.
Kalidindi, S. R., Gomberg, J. A., Trautt, Z. T., & Becker, C. A. (2015). Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets. Nanotechnology, 26(34), 344006. https://doi.org/10.1088/0957-4484/26/34/344006
Kalidindi, Surya R, et al. "Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets." Nanotechnology vol. 26,34 (2015): 344006. doi: https://doi.org/10.1088/0957-4484/26/34/344006
Kalidindi SR, Gomberg JA, Trautt ZT, Becker CA. Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets. Nanotechnology. 2015 Aug 28;26(34):344006. doi: 10.1088/0957-4484/26/34/344006. Epub 2015 Aug 03. PMID: 26235174.
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Nanotechnology. 2015 Aug 28;26(34):344006. doi: 10.1088/0957-4484/26/34/344006. Epub 2015 Aug 03.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets.

Nanotechnology

Surya R Kalidindi, Joshua A Gomberg, Zachary T Trautt, Chandler A Becker

Affiliations

  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

PMID: 26235174 DOI: 10.1088/0957-4484/26/34/344006

Abstract

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

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