(103a) Augmented Property Prediction of Ionic Liquids by Means of a Gradient-Based Optimization Workflow

Reith, D. - Presenter, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
Köddermann, T. - Presenter, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)
Hülsmann, M. - Presenter, Bonn-Rhein-Sieg University of Applied Sciences

Applications of ionic liquids range from environmentally friendly solvents for novel synthesis, to electrolyte devices, such as batteries and photochemical cells. For all of them, the special physical properties of ionic liquids are a key feature. In particular, the transport properties are crucial when considering the reaction kinetics in a synthesis process or ion transport in electrochemical devices. In this contribution, we show that insight from molecular dynamics (MD) simulations can augment the development of new industrial innovations. Using an appropriate force field, MD simulations are capable of describing transport properties such as self diffusion coefficients, viscosities and conductivities for imidazolium based [C2mim][NTf2] and it's mixtures with chloroform and water. As we will show, our recently developed Gradient-based Optimisation Workflow (GROW) for the automated development of molecular models significantly enhances the efficient parametrization of force fields.

The simulated data not only reproduce the experimental values excellently, we could also find the experimentally observed maximum of the conductivity for the [C2mim][NTf2]/chloroform mixtures. To the best of our knowledge it is the first time that this conductivity maximum for Ils is found in MD simulations. It will also be shown that this conductivity behaviour is related to the formation of neutral IL clusters rather than the formation of contact ion pairs.