Interactions of fluids with solid interfaces play an important role in many processes, though the underlying interaction mechanisms are often poorly understood. Molecular simulations allow for an insight into the systems on a molecular level to enable the analysis of relevant interaction patterns and with this the identification of dominating influencing factors. Though these MD studies require for a start the development of suitable parametrization approaches for the molecular models used in the simulation to describe the properties of the compounds.
Subject of a current cooperation with the group of Prof. Garnweitner from the IPAT is the detailled analysis of the interactions of the solvent with ZnO nanoparticles during their synthesis. The project is aimed at gaining a fundamental understanding of the interaction mechanism between the nanoparticle and the solvent to allow for a tailoring of the reaction system for different applications.
In this project, a novel genetic algorithm was developed for the parameterization of interfacial force fields using ab initio and ab initio MD simulations as well as methods of machine learning. This new approach has shown that it is possible to successfully transfer the accuracy of ab initio simulations into the higher-scale MD calculations for a H2-ZnO test system. In the further course of this project, various molecular models for the description of the interactions between different solvents and different ZnO nanoparticle surfaces will be optimized. With the optimized force field, characteristic microstructural properties of the solvent on the surface of ZnO nanoparticles can be predicted and compared with experimental data.
