From quantum mechanics to mesoscale science: Large scale ab-initio simulations of materials

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This NSERC Discovery grant project concerns the computation of materials properties from ab-initio first principles density functional theory. Many important properties of crystalline solids including metals and insulators are mediated through defects in the crystal structure even when they occur in very dilute concentrations. Thus, a predictive understanding of material properties requires an understanding of the defects. Unfortunately this is challenging, as defects intimately couple the complex chemistry of the broken bonds in the core, the discrete atomistic nature of the annular region, and the long-range slow decay of continuum elastic fields. A number of multiscale approaches have been proposed to address this, but these require asymptotic assumptions or ad hoc patches that require case-specific expertise in their implementation. These are not only counter to the ab-initio philosophy, but also restrict their transferability and their predictive ability. In contrast, the proposed project pursues an approach where DFT is the sole input and controlled numerical approximations enable the study of detects at realistic concentrations. This project has three goals. The first is to establish MacroDFT as one of the standard tools for performing large scale ab-initio simulations. We seek to investigate how remarkable properties of matter emerge from complex correlations of the atomic and electronic constituents and how we can control them by multiscale-modeling and simulations using solely DFT. The second goal of this project is a systematic study of defects in Magnesium and its alloys. Mg-alloys have some of the highest strength-to-weight ratios amongst metals, and Mg is abundant. This makes it attractive for a variety of applications, but this has failed due to its limited ductility. Therefore, we seek to improve the mechanical properties of new Mg-based alloys by increasing its ductibility and formability. New Mg-alloys are anticipated to play a critical role in the nation's transportation energy and environmental future according. The Government of Canada has set a target of reducing total greenhouse gas (GHG) emissions by 45-65 percent by 2050 by reducing cars weight using new Mg-alloys. Therefore, the main finding of this research projects will have a high impact in Canada's research and industry applications. The third goal of this proposal is the computational engineering of graphene-based transistors using mesoscale models based solely on ab-initio calculations. We seek to design band gaps in nanodevices made of graphene and its variants through defects. This is important since it can be applied to the new generation of ultra-small and ultra-fast electronic applications. We finally note that while we currently propose to focus on Mg and graphene in this project, DFT is applicable to all materials and thus MacroDFT is applicable to all crystalline solids.