Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
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Date
2012-10-14Type
- Journal Article
ETH Bibliography
yes
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Abstract
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon. Show more
Publication status
publishedExternal links
Journal / series
The Journal of Chemical PhysicsVolume
Pages / Article No.
Publisher
American Institute of PhysicsOrganisational unit
03499 - Koumoutsakos, Petros (ehemalig) / Koumoutsakos, Petros (former)
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ETH Bibliography
yes
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