Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters

Rizzi, Francesco; Najm, Habib N.; Debusschere, Bert; Sargsyan, Khachik; Salloum, Maher; Adalsteinsson, Helgi; Knio, Omar M.

doi:10.1137/110853170

Cited by 81 publications

(81 citation statements)

References 31 publications

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“…On the fundamental side, interesting generalizations include the potential of leveraging the present approach for the purpose of model reduction, [33][34][35][36] and for quantifying the impact of uncertainties that may affect system parameters. 22,[37][38][39][40][41][42][43] In particular, such extensions would offer the promise of accounting for and distinguishing between the impacts of parametric sensitivity and irreducible noise, 43 as well as providing estimates where reduced model approximations are valid. These topics are the subject of ongoing work.…”

Section: Discussionmentioning

confidence: 99%

Variance decomposition in stochastic simulators

2015

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Articles you may be interested inThis work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models. C 2015 AIP Publishing LLC. [http://dx

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Section: Discussionmentioning

confidence: 99%

Variance decomposition in stochastic simulators

2015

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“…Recent Bayesian formulations include [13,2]. We use a 5-site water model, TIP5P [12] and we resolve long range electrostatics using the ParticleMesh-Ewald method.…”

Section: Data and Modelsmentioning

confidence: 99%

Hierarchical Bayesian Modeling for Molecular Dynamics Simulations Using Heterogeneous Data

Wu¹,

Angelikopoulos²,

Tauriello³

et al. 2015

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. Heterogeneous data are often used to calibrate the parameters of Molecular Dynamics (MD) simulations. The inference of the MD model parameters from such data may lead however to significant inconsistencies. We propose a hierarchical Bayesian framework to systematically integrate heterogeneous data in MD simulations and demonstrate our approach in simulations of liquid water. The MD parameters are calibrated with a set of experimental data for diffusivity, radial distribution function and density of water. Furthermore, we tackle the high computational demands associated with hierarchical Bayesian models by combining a surrogate model based on the Empirical Interpolation Method with a parallelized sampling-based estimation scheme. We discuss extensions of this approach to the calibration of MD simulations of other systems using heterogeneous experimental data.

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“…Various approaches have been developed in order to overcome this issue. [42][43][44][45]48 In the present work, application of specific techniques to filter out noise proved unnecessary, because of (i) our ability to perform a very large number of MC samples and (ii) to consider multiple replica. As a result, the obtained estimates exhibit sufficient smoothness, which justifies the application of direct projection technique.…”

Section: Introductionmentioning

confidence: 97%

“…In fact, those studies have worked with Molecular Dynamics (MD) instead of MC molecular simulations. For instance, Rizzi et al have demonstrated a successful coupling between PC and MD in studying force field parameters of water molecules 42,43 in addition to concentration driven ionic flow in nano-pores. 44,45 Later on, similar coupling is adopted in order to investigate flow at nanoscale 46 and quantify parametric uncertainty in multi-scale simulations.…”

Section: Introductionmentioning

confidence: 99%

Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

Kadoura

Siripatana

Sun

et al. 2016

The Journal of Chemical Physics

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In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercritical isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH4, N2, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO2 and C2 H6.

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Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters

Cited by 81 publications

References 31 publications

Variance decomposition in stochastic simulators

Variance decomposition in stochastic simulators

Hierarchical Bayesian Modeling for Molecular Dynamics Simulations Using Heterogeneous Data

Single-site Lennard-Jones models via polynomial chaos surrogates of Monte Carlo molecular simulation

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