SISSA Mathlab seminar announcement
Emanuele Tuillier Illingworth
tuillier at sissa.it
Mon May 5 09:08:40 CEST 2014
MATHLAB SEMINAR ANNOUNCEMENT
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,
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
Using Polynomial Chaos (PC) expansions and Bayesian inference, we
develop a framework
that enables us to describe the impact of parametric uncertainty on the
and, at the same time, properly quantify the effect of the intrinsic noise.
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