SISSA Mathlab seminar announcement

Emanuele Tuillier Illingworth tuillier at
Mon May 5 09:08:40 CEST 2014


Francesco Rizzi, Ph.D.
Dept. of Mechanical and Materials Science
Duke University, NC, USA

Venue: Wednesday 21 May at 11:30 am in lecture room 133

Uncertainty Quantification in Molecular Dynamics Simulations

Molecular Dynamics (MD) simulations provide a suitable tool to explore 
the properties of
a system at the atomic level which, in general, are difficult and 
expensive to investigate experimentally.
The main weakness of MD is that its predictive reliability depends on 
the accuracy with which
the MD potential function can model the atomic interactions occurring in 
the real system of interest.
Consequently, defining the potential is the most delicate stage of an MD 
This is typically done in a deterministic setting, namely by choosing 
specific values for
the parameters of the MD potential. Literature, however, shows that for 
most MD systems,
these parameters are characterized by broad uncertainties. Uncertainty 
quantification (UQ) can
thus play a key role for quantifying these uncertainties, and properly 
characterize the predictive accuracy.

This talk shows a possible approach for applying UQ methods to MD 
Two fundamental, distinct sources of uncertainty are investigated, 
namely parametric
uncertainty and intrinsic noise. Intrinsic noise is inherently present 
in the MD setting,
due to fluctuations originating from thermal effects. Parametric 
uncertainty, on the contrary,
is introduced in the form of uncertain potential parameters, geometry, 
and/or boundary conditions.
We illustrate the use of a probabilistic (Bayesian) approach to infer 
parameters for MD simulations of pure water using data of selected 
macroscale observables.
Using Polynomial Chaos (PC) expansions and Bayesian inference, we 
develop a framework
that enables us to describe the impact of parametric uncertainty on the 
MD predictions
and, at the same time, properly quantify the effect of the intrinsic noise.

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