(Oct 24th) Order N computational Methods for simulating large scale models of complex Matter

I will introduce multiscale simulation techniques and predictive modeling based on order N algorithms. Such methodologies have a computational cost which scales linearly with the number of atoms enabling simulations of physical properties in realistic models of very large system sizes (with up to 1 billion atoms), reaching the experimental and technology scales. The numerical tools will be shown to allow the study of charge and thermal transport in chemically and structurally complex forms of graphene accounting from substrate effects, polycrystalline morphology of CVD graphene (and hBN), and chemically functionalization; all aspects being of crucial relevance for the development of applications in flexible and transparent electronics or energy harvesting. After introducing some challenges about the modeling of graphene composites I will present a quantitative analysis of charge and thermal transport properties in graphene materials in presence of structural imperfections as produced during the wafer-scale production of graphene through chemical growth (CVD), the chemical transfer to versatile substrates, and the device fabrication. Fundamental properties of charge mobilities in polycrystalline graphene, accounting the variability in average grain sizes and chemical reactivity of grain boundaries as observed in real samples grown by CVD will be presented, together with their relevance for device optimisation and diversification of applied functionalities such as chemical sensing.