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IMA 2013 UQ: Bayesian Calibration of Molecular Dynamics Simulations for Composite Materials Properties

By Paul N. Patrone

College of Science and Engineering, University of Minnesota, Minneapolis, MN

Published on


In recent years, the composites community has increasingly used molecular dynamics to simulate and explore material properties such as glass-transition temperature and yield strain. In virtually all such simulations, a key challenge is to select one or more input structures that represent the real polymer matrix at the nanoscale. Often an appropriate choice of inputs is not known a priori, which can lead to a large uncertainty in the simulated composite properties. In this talk, I discuss ongoing research whose goal is to determine, via Bayesian inference, an ensemble of inputs that represents a class of commercially important amine-cured epoxies. We construct an analytical approximation (i.e. a surrogate or emulator) of the simulations, treating the input structure energy and size as adjustable calibration parameters. By training the emulator with experimental results, we will determine a posterior distribution (or probability) that a given set of calibration parameters corresponds to the real systems.

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Researchers should cite this work as follows:

  • Paul N. Patrone (2014), "IMA 2013 UQ: Bayesian Calibration of Molecular Dynamics Simulations for Composite Materials Properties,"

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Lind Hall, University of Minnesota, Minneapolis, MN

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