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Uncertainty Quantification in Molecular Dynamics Studies of the Glass Transition Temperature

Published

Author(s)

Paul N. Patrone, Andrew Dienstfrey, Andrea R. Browning, Samuel Tucker, Stephen Christensen

Abstract

Industry and other institutions alike are increasingly using molecular dynamics (MD) simulations to inform their materials development decisions. Concurrently, there is growing awareness that, in order to make such strategies routine and reliable, simulated predictions should be accompanied by quantitative statements of their quality. The overall body of analysis that strives to accomplish this task is referred to as uncertainty quantification (UQ). In the following we develop a host of UQ tools designed to assess the quality of computational data and estimate uncertainty in simulated predictions of the glass transition temperature Tg. We consider several contributions to this uncertainty arising from: (i) identification of asymptotic regimes in density versus temperature relations; (ii) fluctuations associated with limited time-averaging and dynamical noise; (iii) and partial averaging over polymer-network configurations due to restrictions in system size. We present a sequence of analyses by which we assess each of these contributions and quantify their net effect on simulation-based estimates of Tg . Importantly, these methods could lead to more efficient workflows by indicating when multiple small simulations can be combined to yield estimates with uncertainties comparable to larger, more expensive simulations. We expect that related approaches will, in the future, be applicable to other physical quantities of interest as well as to a broader class of computational tools.
Citation
Macromolecular Theory and Simulations

Keywords

polymers, glass transition temperature, uncertainty quantification, molecular dynamics

Citation

Patrone, P. , Dienstfrey, A. , Browning, A. , Tucker, S. and Christensen, S. (2016), Uncertainty Quantification in Molecular Dynamics Studies of the Glass Transition Temperature, Macromolecular Theory and Simulations, [online], https://doi.org/10.1016/j.polymer.2016.01.074 (Accessed September 27, 2023)
Created March 21, 2016, Updated October 12, 2021