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

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.

Development of high throughput assays for establishing process-structure-property linkages in multiphase polycrystalline metals: Application to dual-phase steels

“…One of the key elements of the high throughput assays described in the paper is in the use of A second key element of the high throughput assays described here is in the use of the emerging data science techniques for formulating the reduced-order PSP linkages. The efficacy is derived from the ability to template the workflows in ways that they can be applied to a broad range of material systems over a multitude of length/structure scales [68,127,[135][136][137][138]. However, the use of a common template does not imply lack of flexibility.…”

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

“…In prior work, the protocols described above have been successfully applied to multiphase composite systems [13,14,19,43,44,57,58], atomistic datasets [59,60], and polycrystalline microstructures [42,61]. However, in all of these applications, the microstructure domains had a simple overall shape (rectangles in 2-D and rectangular parallelepipeds in 3-D), periodicity was generally imposed to take advantage of FFT algorithms, and the studies used relatively small domains.…”

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

“…As mentioned earlier, this step is central to the extraction of transferrable materials knowledge needed in multiscale materials modeling efforts [69]. Although it is possible to select a number of different measures for the quantification of the microstructure in this work, the most logical choice here would be chord length distributions (CLDs).…”