Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.

ABSTRACT: A high level of physical detail in a molecular model improves its ability to perform high accuracy simulations but can also significantly affect its complexity and computational cost. In some situations, it is worthwhile to add complexity to a model to capture properties of interest; in others, additional complexity is unnecessary and can make simulations computationally infeasible. In this work, we demonstrate the use of Bayesian inference for molecular model selection, using Monte Carlo sampling techniques accelerated with surrogate modeling to evaluate the Bayes factor evidence for different levels of complexity in the two-centered Lennard-Jones + quadrupole (2CLJQ) fluid model. Examining three nested levels of model complexity, we demonstrate that the use of variable quadrupole and bon

SUBMITTER: Madin OC 

PROVIDER: S-EPMC9217127 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature