We explore the usage of Bayesian inference as a force field parameterization paradigm and the techniques required to make these Bayesian decisions effectively. Current force field parameterization strategies allow for the optimization of certain parameters, but have difficulty effectively sampling multimodal or shallow distributions. Another critical deficiency of these methods is their inability to compare multiple functional forms. Deciding which functional forms or levels of complexity are required will be an important consideration in developing the next generation of biomolecular force fields.<br/>Reversible jump Monte Carlo (RJMC) facilitates equilibrium sampling of multiple models and their respective parameters, allowing for the estimation of probability distributions for both models and parameters. Statistical evidence in the form of Bayes factors can be taken from these distributions and allows for comparison of the relative fitness of force fields. We test this strategy on previously developed physical property surrogate models for the 2-center Lennard Jones plus quadrupole (2CLJQ) force field, allowing for fast comparison between surrogate model outputs and experimental data. RJMC sampling shows that, while the variable bond length of the 2CLJQ force field is justified for the studied diatomics and other similar molecules, the quadrupole parameter is not justified for use in the reproduction of physical properties such as density, saturation pressure, and surface tension. This analysis also shows that for other molecules, with larger expected quadrupole moments, the quadrupole parameter is required to reproduce these physical properties. This study shows the ability of RJMC to distinguish between cases where a certain parameter or functional form is justified or not.<br/>In order to generalize this strategy, we also propose and test techniques to generate surrogate models “on-the-fly” for more general sets of parameters, using a multifidelity approach of equilibrium simulation, mixture reweighting with MBAR, and Gaussian process modeling to generate analytical surfaces as a function of force field parameters such as Lennard Jones epsilon and sigma. We demonstrate this technique on relatively simple molecular systems and discuss future plans for using these techniques to rationally design better small-molecule biomolecular force fields, as a part of the Open Force Field Initiative.

Force field complexity assessed via Bayesian inference and reversible jump Monte Carlo sampling

2019 Fall National Meeting

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