Thermophysical properties of deep eutectic solvents: simulation and data analysis

In collaboration with Dr. Gudrun Gygli, KIT, and Prof. Niels Hansen, University of Stuttgart.

Deep eutectic solvents are promising non-conventional media for biocatalysis. However, their application is limited by their high viscosity at ambient temperature. In this project, we explore the effect of adding water and other components to the viscosity of selected deep eutectic solvents. The project consists of two parts:

1. Performing systematic molecular dynamics simulations to learn about the molecular basis of viscosity in complex mixtures. To enable large parameter studies, we develop a workflow based on Jupyter Notebook and openMM as a "Simulation Foundry".

People: Benjamin Schmitz, Henrique Carvalho


  1. Carvalho, H., Ferrario, V., & Pleiss, J. (2021). The molecular mechanism of methanol inhibition in CALB-catalyzed alcoholysis: analyzing molecular dynamics simulations by a Markov state model. J Chem Theory Comput, 17, 6570–6582.
  2. Gygli, G., & Pleiss, J. (2020). Simulation Foundry: automated and F.A.I.R. molecular modelling. J Chem Inf Model, 60, 1922–1927.
  3. Baz, J., Held, C., Pleiss, J., & Hansen, N. (2019). Thermophysical properties of glyceline-water mixtures investigated by molecular modelling. Phys Chem Chem Phys, 21, 6467–6476.

2. Data integration and analysis of simulated and experimental data. To enable a systematic comparison of data from experiment and from simulation, we use the data exchange format ThermoML to extract data from literature and from our simulations.

People: Matthias Gültig, Jan Range


  1. Gygli, G., Xu, X., & Pleiss, J. (2020). Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Sci Rep, 10, 21395–21395.
  2. Xu, X., Range, J., Gygli, G., & Pleiss, J. (2020). Analysis of thermophysical properties of deep eutectic solvents by data integration. J Chem Eng Data, 65, 1172–1179.
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