VFT and Arrhenius Modelling

Storage of thermophysical properties (e.g. density, viscosity, thermodynamic activity of water, conductivity) using Chemical Markup Language (CML).

This is a collection of deep eutectic solvent (DES) experimental and simulation data that is stored in CML format and analysed using gradient boosting decision trees.

A meta-analysis of the impact of water content and temperature on the viscosities of four deep eutectic solvents (glyceline, reline, DEAG, DEACG), their components (choline chloride, urea, glycerol, ethylene glycol), methanol, and pure water was performed. We analyzed the viscosity data by an automated workflow, using Arrhenius and Vogel–Fulcher–Tammann-Hesse models.

Python script wrapping up (executing) all the steps of the workflow the user enters in it. Together with "names.py" this script requires input files to be located in a folder called "input" in the same directory.

Input data (viscosities, in .csv format) for the modelling workflow, collected from literature, see associated publication for details.

Resulting data from the modelling workflow, see associated publication.

The modelling workflow used on the input data, which leads to the results, see associated publication.

Python script allowing the user to define the names of the input files to be used. Together with "wrapper.py" this script requires input files to be located in a folder called "input" in the same directory.

Instructions and details on the data analysis workflow.