CML processing and analysis

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.

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

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.

Contains exp.csv, a collection of experimental data of CholineChloride:Glycerol:Water mixtures.
Contains sim.csv, a collection of molecular dynamics simulation data of CholineChloride:Glycerol:Water mixtures.
Contains Modelling_exp_Figure3.csv, a collection of modelled Eeta (energy activation of viscous flow), lnEta0 (viscosity at infinite temperature) values of CholineChloride:Glycerol:Water mixtures, based on experimental data, see the associated publication for details.
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