Summerschool on theoretical modelling at the nanoscale

This School will focus on theoretical training on subjects that play a fundamental role in nanoscale science, condensed matter physics, materials science, and bioengineering.

Simulations are an invaluable tool to make predictions and bring physical understanding that cannot be achieved by experimental techniques alone, and to provide atomic-scale insights to interpret, understand and verify experimental findings. In particular, the field of computer simulations and theoretical modelling has witnessed a substantial progress in the past years, especially due to the incorporation and development of big-data and machine-learning techniques tailored for condensed matter physics, materials science and bioengineering. So called ab initio methods have long been the “holy grail” of simulations relying only on fundamental physical laws and constants instead of empirical parameters or idealized models. Under well-known approximations these first-principles techniques allow to investigate the electronic structure and the interactions in molecules, bulk materials, and surfaces. However they are often computationally too expensive to be of any practical use for a large variety of problems. Consequently, ab initio techniques were proven to be more powerful in connection with dynamical and thermodynamical algorithms, and their reach has been greatly extended by their combination with machine-learning algorithms. The latter significantly increased the number of systems that can be treated by profiting from the lower computational effort. From another perspective, these simulations and techniques also allow a better parametrization of empirical models, making multiscale approaches more reliable and predictive simulations of large complex systems possible.