Parameter Study of Solvent Systems by Molecular Dynamics Simulations (Project: EnzSim)

Characterizing the dependence of the thermophysical properties of complex liquid mixtures on parameters such as composition and temperature is pivotal to the choice of an optimal solvent in process engineering. Therefore, it is indispensable to perform comprehensive parameter studies for the exploration of design space. Molecular simulation is a powerful tool for the prediction of properties under conditions that have not yet been explored experimentally. However, simulation results have to be calibrated with published experimental data. In order to make experimental and simulated data available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content and temperature. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The usefulness of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds at a specific temperature to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis and publication on Dataverse was demonstrated. Because the Dataverse datasets are in ThermoML format, the data is findable, accessible, interoperable, and reusable (FAIR).