Process data technology

The ongoing transformation of industrial practice by cyber-physical systems is facilitated by a growth of the available computational resources and by improvements in mathematical modelling and simulation. New disciplines and fields are emerging. Computational molecular engineering is the discipline that applies molecular modelling and simulation to characterize thermodynamic processes and properties for process data technology. The employed methods include Monte Carlo (MC) simulation, molecular dynamics (MD) simulation, molecular equations of state, and mesoscopic approaches such as density gradient theory, phase field simulation, and many others.

These methods are computationally demanding. In some cases, this is because many instances of the same problem need to be solved for varying parameters. In the case of molecular simulations of systems containing a great number of molecules, even executing a single simulation is only feasible with scalable massively-parallel simulation codes that facilitate an efficient use of high performance computing (HPC) infrastructure. The number of suitable simulation platforms that can achieve this is limited. One of them is the ls1 mardyn program, which holds the world record for system size in a MD simulation (20 trillion molecules).

The main aims of my research and development work consist in making the level of sophistication that molecular methods have attained over the last two decades fully accessible at the level of industrial engineering practice, and providing bespoke molecular modelling and simulation solutions for industrial use cases.

Research threads