The basic input to nanodcal is the spatial position and chemical composition of the atoms in the nanostructure. The algorithms used this input to create a complete quantum mechanical model of the nanostructure. From this quantum mechanical model, the software can compute a wide range of properties of interest, such as conductance and current-voltage characteristics
These quantum mechanical models are:
- Accurate: nanodcal uses Density Functional Theory (DFT), a first-principles model of the interactions between electrons in the constituent atoms, which is the most accurate quantum mechanical model that is feasible for systems of 100+ atoms
- Versatile: nanodcal is able to describe a wide range of systems because of the transferability of DFT. It is not based on empirical parameters fit to particular materials or systems.
- Efficient: nanodcal is one of the fastest available implementations of DFT. nanodcal uses a Linear Combination of Atomic Orbtials (LCAO) to describe the state of the electrons in the device. Again, this is the most accurate and efficient choice possible for systems of this size and complexity.
- Self-consistent: nanodcal models effects of external fields in a Self-Consistent Field (SCF) approach.
- Conceptually sound: nanodcal uses state-of-the Non-Equilibrium Green's Function (NEGF) theory to compute transport properties of nanoscale structures.