Long-range parameter optimization for a better description of potential energy surfaces using Density Functional Theory

The advance of computing and the development of modern quantum chemistry models such as Density Functional Theory (DFT) have allowed scientists to perform fast in silico studies with accurate results. It also allowed for the achievement of empirically unattainable quantities such as Potential Energy Surfaces (PES), a fundamental construct in various applications, such as the study of weakly bound systems. One of DFT’s current weaknesses is a reliable description of PESs, due to a lack of suitable exchange-correlation functionals. In general, other post-Hartree-Fock methods are employed, such as n th-order Møller-Plesset’s Perturbation Theory (MPn) or Coupled Cluster Theory (CCSD(T)) with large basis sets. Despite producing good results, these methods demand much computational power when applied to large systems. This work presents a novel approach of PES description of the H2O2–Kr system using DFT by optimizing a long-range parameter present in some DFT functionals, obtaining results similar to those of the MPn methods with somewhat less computational time necessary.