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Bayesian calibration of force-fields from experimental data : TIP4P water
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Dutta, Ritabrata, Brotzakis, Zacharias Faidon and Mira, Antonietta (2018) Bayesian calibration of force-fields from experimental data : TIP4P water. The Journal of Chemical Physics, 149 (15). 154110. doi:10.1063/1.5030950 ISSN 0021-9606.
An open access version can be found in:
Official URL: http://dx.doi.org/10.1063/1.5030950
Abstract
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters are calibrated to reproduce properties measured by experiments or simulations. The main contribution of this paper is an approximate Bayesian framework for the calibration and uncertainty quantification of the force-field parameters, without assuming parameter uncertainty to be Gaussian. To this aim, since the likelihood function of the MD simulation models is intractable in the absence of Gaussianity assumption, we use a likelihood-free inference scheme known as approximate Bayesian computation (ABC) and propose an adaptive population Monte Carlo ABC algorithm, which is illustrated to converge faster and scales better than the previously used ABCsubsim algorithm for the calibration of the force-field of a helium system. The second contribution is the adaptation of ABC algorithms for High Performance Computing to MD simulations within the Python ecosystem ABCpy. This adaptation includes a novel use of a dynamic allocation scheme for Message Passing Interface (MPI). We illustrate the performance of the developed methodology to learn posterior distribution and Bayesian estimates of Lennard-Jones force-field parameters of helium and the TIP4P system of water implemented for both simulated and experimental datasets collected using neutron and X-ray diffraction. For simulated data, the Bayesian estimate is in close agreement with the true parameter value used to generate the dataset. For experimental as well as for simulated data, the Bayesian posterior distribution shows a strong correlation pattern between the force-field parameters. Providing an estimate of the entire posterior distribution, our methodology also allows us to perform the uncertainty quantification of model prediction. This research opens up the possibility to rigorously calibrate force-fields from available experimental datasets of any structural and dynamic property.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
| Journal or Publication Title: | The Journal of Chemical Physics | ||||||||
| Publisher: | American Institute of Physics | ||||||||
| ISSN: | 0021-9606 | ||||||||
| Official Date: | 18 October 2018 | ||||||||
| Dates: |
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| Volume: | 149 | ||||||||
| Number: | 15 | ||||||||
| Article Number: | 154110 | ||||||||
| DOI: | 10.1063/1.5030950 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Open Access Version: |
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