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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
This mini-symposium, colocated at the ECMTB in Gothenburg, presents the state-of-the-art in computational methods for multicellular systems biology. It brings together developers and users of this software to identify common approaches and future challenges concerning multiscale integration, model specification, model exchange, scalability, workflow management as well as compliance to standards and guidelines. The session thus aims to provide an overview of the available modeling and simulation
Creator: Walter de Back, TU Dresden
Contributor: Lutz Brusch