Due to the increase of available computer power, new applications in nano science and physics appear such as study of properties of new materials (photovoltaic materials, bio- and environmental sensors, …), failure in materials, nano-indentation. Chemists, physicists now commonly perform simulations in these fields. These computations simulate systems up to billion of atoms in materials, for large time scales up to several nanoseconds. The larger the simulation, the smaller the computational cost of the potential driving the phenomena, resulting in low precision results. So, if we need to increase the precision, there is two ways to decrease the computational cost. In the first approach, we improve classical methods and algorithms and in the second way, we will consider a multiscale approach.

Many applications in material physics need to couple several models like quantum mechanic and molecular mechanic models, or molecular and mesoscopic or continuum models. These couplings allow scientists to treat larger solids or molecules in their environment. Many of macroscopic phenomena in science depend on phenomena at smaller scales. Full simulations at the finest level are not computationally feasible in the whole material. Most of the time, the finest level is only necessary where the phenomenon of interest occurs; for example in a crack propagation simulation, far from the tip, we have a macroscopic behavior of the material and then we can use a coarser model. The idea is to limit the more expensive level simulation to a subset of the domain and to combine it with a macroscopic level. This implies that atomistic simulations must be speeded up by several orders of magnitude.

We will focus on two applications ;the first one concerns the computation of optical spectra of molecules or solids in their environment. In the second application, we will develop faster algorithms to obtain a better understanding of the metal plasticity, phenomenon governing by dislocation behavior. Moreover, we will focus on the improvement of the algorithms and the methods to build faster and more accurate simulations on modern massively parallel architectures.