The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively optimize three-site water models to fit macroscopic (average) thermodynamic properties, providing “state-of-the-art” three-site models that still present some deviations from the liquid water properties. Here we show results obtained by automatically optimizing three-site rigid water models to fit a combination of microscopic and macroscopic experimental observables. We use Swarm-CG, a multi-objective particle-swarm-optimization algorithm, for training the models to reproduce the experimental radial distribution functions of liquid water at various temperatures (rich in microscopic-level information on, e.g., the local orientation and interactions of the water molecules). We systematically analyze the agreement of these models with experimental observables and the effect of adding macroscopic information into the training-set. Our results demonstrate how adding microscopic-rich information in the training of water models allows achieving state-of-art accuracy in an efficient way. Limitations in the approach and in the approximated description of water in these three-site models are also discussed, providing a demonstrative case useful for the optimization of approximated molecular models in general.
Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables
27 July 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.