title: A hierarchical Bayesian framework for force field selection in molecular dynamics simulations.
abstract: We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.
authors.affiliation: Professorship for Computational Science, Clausiusstrasse 33, ETH-Zurich 8092, Switzerland.
authors.forename: S
authors.initials: S
authors.lastname: Wu
authors.affiliation: Professorship for Computational Science, Clausiusstrasse 33, ETH-Zurich 8092, Switzerland.
authors.forename: P
authors.initials: P
authors.lastname: Angelikopoulos
authors.affiliation: Department of Mechanical Engineering, University of Thessaly, Leoforos Athinon, Pedion Areos, Volos 38334, Greece.
authors.forename: C
authors.initials: C
authors.lastname: Papadimitriou
authors.affiliation: Institute of Computational Engineering and Science, UT-Austin, 201 East 24th Street, Stop C0200, Austin, TX 78712-1229, USA.
authors.forename: R
authors.initials: R
authors.lastname: Moser
authors.affiliation: Professorship for Computational Science, Clausiusstrasse 33, ETH-Zurich 8092, Switzerland petros@ethz.ch.
authors.forename: P
authors.initials: P
authors.lastname: Koumoutsakos
keywords: hierarchical Bayesian; model selection; molecular dynamics