Tight binding parameterization through particle swarm optimization algorithm

The tight binding (TB) approach represents a good trade-off between accuracy and computational burden. For this reason, it is widely used for device simulations. However, a proper description of a physical system by means of TB requires an accurate parameterization of the Hamiltonian matrix elements (HME), that is usually done by fitting over suitable properties that can be measured or computed with first-principles approaches. We show that the particle swarm optimization algorithm is a powerful tool for the parameterization of the TB HME, using the density functional theory band dispersions of bulk reference materials as a target. We discuss the results obtained for bulk MAPbI
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perovskite in its high temperature cubic phase.