Atomistic computer simulations of processes occurring at the water/oxide interface are challenging in several ways. Not only is the calculation of atomic forces based on ab initio methods computationally very demanding, but also barrier-crossing events may lead to computationally taxing time scales, severely limiting accessible system sizes and simulation times.
In project P12, we will address these challenges using a combination of machine-learning and advanced rare event sampling methods. In particular, using software developed in our group and collaborating with P03 Kresse, we will train neural network potentials based on the Behler-Parrinello approach for oxide/water interfaces, starting with the Fe3O4/water system studied in P11 Backus
If you are interested, please send your applications to Christoph Dellago.