Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
Bayesian uncertainty quantification and propagation in molecular dynamics simulations: A high performance computing framework
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DOI：
10.1063/1.4757266
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发表时间：
2012-10-14
影响因子：
4.4
通讯作者：
Koumoutsakos, Petros
中科院分区：
文献类型：
--
作者：
Angelikopoulos, Panagiotis;Papadimitriou, Costas;Koumoutsakos, Petros
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. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4757266]
