Calibration and ranking of coarse-grained models in molecular simulations using bayesian formalism

Understanding and prediction of the performance of complex materials using molecular simulations, as well as the design of a new generation of materials with desired functionality, depend on the predictive capacity of the coarse-grained models that enable reduced computational cost. Depending on what aspect of the behavior is of interest, we often have at our disposal various coarse-grained models with different predictive capabilities. In this work, focusing on coarse-grained water models, we demonstrate how the plausibilities of these models are relatively compared, and how predictions can be made exploiting the ensemble. Using a Bayesian model ranking framework, we will show the plausibility results which are in agreement with experts’ expectation on how these models rank in terms of predicting different quantities of interest, and how different models can be mixed and produce an ensemble prediction with higher accuracy.