Fully Atomistic Modeling of Realistic Plasmonic Materials: Assessing the Performance of Iterative Solvers

The fully atomistic modeling of real-size plasmonic nanostructures is computationally demanding, therefore most calculations are limited to small-to-medium sized systems. However, plasmonic properties strongly depend on the actual shape and size of the samples. In this paper we substantially extend the applicability of classical, fully atomistic approaches by exploiting state-of-the-art numerical iterative Krylov-based techniques. In particular, we focus on the recently developed $ω$FQ model, when specified to carbon nanotubes, graphene-based nanostructures and metal nanoparticles. The performance of Generalized Minimal Residual (GMRES) and Quasi-Minimum Residual (QMR) algorithms is studied, with special emphasis on the dependence of the convergence rate on the dimension of the structures (up to 1 million atoms) and the physical parameters entering the definition of the atomistic approach.

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