Juergen Pleiss

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.

Determination of thermophysical properties (densities, diffusion coefficients, energy contributions) of different methanol-water mixtures using molecular dynamics simulations.

This is a collection of data involving choline chloride:glycerol:water mixtures stored in CML.

This is a collection of data that have been used to analyse data on deep eutectic solvent mixtures of choline chloride:glycerol:water.

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.

A collection of python scripts used to generate CML from csv (CSV_to_CML.py), apply machine learning (Gradient Boosting using decision trees, prediction_viscosity.py), model based on eq 6 in the associated publication, and to plot the generated data (plot*.py).

Contains CML dictionaries created to store deep eutectic solvent data with CML.

Contains CML created for experimental, simulation and predicted data. Predictions were made based on experimental data using a gradient boosting decision tree.

CML dictionary for compchem conventions allowing representation of thermophysical properties of deep eutectic solvents (DES) with CML. Examples of thermophysical properties are: density, viscosity, conductivity and water activity

CML unit dictionary for mili pascal * second and microSiemens per cm

Instructions and details on the data analysis workflow.