Calculation of drag reduction in graphene through neural-network molecular dynamics simulations

The researcher will study drag reduction in graphene with a combination of electronic structure methods and large-scale atomistic molecular dynamics (MD) powered by machine learning and high-performance computing (HPC). The first part of the project involves the development of neural-network interatomic potentials for graphene on metallic substrates under air gasses, including the training on density-functional theory (DFT) calculations. The second part involves large-scale MD simulations targeting the comprehensive understanding of the microscopic flow characteristics near graphene surfaces and the impact of surface microstructure on slip.

The position requires robust and documented expertise in the use of electronic-structure simulation software for DFT simulations (e.g. Quantum ESPRESSO) and/or MD (e.g. LAMMPS).