The group research portfolio is structured around computational materials design with more emphasis on computational methods development and device physics & engineering. Many of our research activities need experimental work and hence we have been collaborating with few experimental teams. In these collaborations, we cover the theoretical and computational parts and the colleagues in the experimental teams handle the fabrications and experiments. Such interdisciplinary activities are inevitable considering the extensions of considered problems.
We are working currently in three main research themes; namely the development and applications of new methods for large-scale atomistic calculations, machine learning for high throughput computational materials design, and multi-objective multi-scale inverse computational systems and devices design. The aim of the 1st theme is to enable highly accurate atomistic calculations of electronic systems with at least multi-million atoms. This requires an interactive development involving applied mathematics and mathematical physics. The aim of the 2nd theme is to apply machine learning to have better understanding for long-standing problems in first principle calculations and also applications of machine learning in materials discovery and model developments. The aim of the 3rd theme is to develop and apply a robust multi-scale framework to concurrently and computationally design materials and devices for a wide range of applications. This shall allow developing more accurate and efficient tools with improved predictions. The below figure summarizes