Long Range Parameter Optimization For The Description Of Potential Energy Surfaces Using Density Functional Theory

In Atomic and Molecular Physics, the search for faster, more accurate and efficient methods is constant. The advance of computing has allowed physicists a new platform for evaluation and prediction of results through \textit{in silico} studies. One of the current problems is the description of potential energy surfaces (PESs) through the Density Functional Theory (DFT), since the implementation of this method has produced unsatisfactory results in this description due to the lack of accurate exchange-correlation functionals. In general, the Møller-Plesset Perturbation Theory (MP) or other correlated methods derived from the Hartree-Fock theory are used in these cases. Despite producing good results, the MP method demands a lot of computational power when applied to large systems. This project aims to present a new way of using DFT in the construction of SEPs through the optimization of a long-range parameter present in some DFT functionals, with the hope of obtaining similar results to the MP method with less computational power required.