Storage of thermophysical properties (e.g. density, viscosity, thermodynamic activity of water, conductivity) using Chemical Markup Language (CML).
Programme: This Project is not associated with a Programme
SEEK ID: https://fairdomhub.org/projects/157
Public web page: Not specified
Organisms: No Organisms specified
FAIRDOM PALs: No PALs for this Project
Project created: 20th Aug 2019
Institutions: University of Stuttgarthttps://orcid.org/0000-0001-9119-1778
Well rounded biologist/biotechnologist/biochemist/enzymologist/bioinformatician/computational biologist.
This is a collection of data that have been used to analyse data on deep eutectic solvent mixtures of choline chloride:glycerol:water.
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.
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 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.
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
Investigations: No Investigations
Studies: No Studies
Assays: No Assays