This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Muniz, Maria Carolina; Gartner III, Thomas E.; Riera, Marc; Knight, Christopher; Yue, Shuwen; Paesani, Francesco; Panagiotopoulos, Athanassios Z.
This dataset contains all data (including input files, simulation trajectories as well as other data files and analysis scripts) related to the publication "Vapor-liquid equilibrium of water with the MB-pol many-body potential" by Muniz et al. in preparation (2021). In this work, we assessed the performance of the MB-pol many-body potential with respect to water's vapor-liquid equilibrium properties. Through the use of direct coexistence molecular dynamics, we calculated properties such as coexistence densities, surface tension, vapor pressures and enthalpy of vaporization. We found that MB-pol is able to predict these properties in good agreement with experimental data. The results attest to the chemical accuracy of MB-pol and its large range of application across water's phase diagram.