Material Science

The first project, called iGUESS (Intelligent GUess for Electronic Structure Simulations), will construct a set of optimized basis functions for density functional theory (DFT) simulations and of an optimal initial density to be used in a self-consistency cycle. The team will investigate the possibility to use methods of data analytics and machine learning to achieve a significant performance boost of extreme scale ab-initio simulations in materials science.

The second project, called DATA EXPRESS, focuses on live processing data coming from mainly in-situ transmission electron microscope (TEM) experiments. These advanced TEMs have multiple detectors, which can acquire GBs of data in seconds. Currently, it is often only possible to see if the in-situ experiments were successful during post processing. For that reason, live processing requires a combination of scalable architecture and suitable implementations of algorithms for TEM applications. This project will allow the small changes in device structure to be revealed in real time and as such improve the control and success of these complicated in-situ experiments. The LiberTEM open-source software platform for data logistics and distributed processing is developed for this application and can be a basis for developments in DATA EXPRESS.