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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