Predicting the electronic properties of 3D, million-atom semiconductor nanostructure architectures

The past ∼10 years have witnessed revolutionary breakthroughs both in synthesis of quantum dots (leading to nearly monodispersed, defect-free nanostructures) and in characterization of such systems, revealing ultra narrow spectroscopic lines of <1 meV width, exposing new intriguing effects, such as multiple exciton generation, fine-structure splitting, quantum entanglement, multiexciton recombination and more. These discoveries have led to new technological applications including quantum computing and ultra-high efficiency solar cells. Our work in this project is based on two realizations/observations: First, that the dots exhibiting clean and rich spectroscopic and transport characteristics are rather big. Indeed, the phenomenology indicated above is exhibited only by the well-passivated defect-free quantum dots containing at least a few thousand atoms (colloidal) and even a few hundred thousand atoms (self assembled). Understanding the behavior of nanotechnology devices requires the study of even larger, million-atom systems composed of multiple components such as wires+dots+films. Second, first-principles many-body computational techniques based on current approaches (Quantum Monte-Carlo, GW, Bethe-Salpeter) are unlikely to be adaptable to such large structures and, at the same time, the effective mass-based techniques are too crude to provide insights on the many-body/atomistic phenomenology revealed by experiment. Thus, we have developed a set of methods that use an atomistic approach (unlike effective-mass based techniques) and utilize single-particle + many body techniques that are readily scalable to ∼10³−10⁶ atom nanostructures. New mathematical and computational techniques have also been developed to accelerate our calculations and go beyond simple conjugate gradient based methods allowing us to study larger systems. In this short paper based on a poster presented at the DOE SciDAC06 conference we will present the overall structure as well as highlights of our computational nanoscience project.