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

Area of Science:

• Quantum Chemistry
• Computational Physics
• Chemical Dynamics

Background:

• Density Functional Theory (DFT) enables rapid in silico studies with accurate results.
• Potential Energy Surfaces (PES) are crucial for understanding molecular interactions, especially in weakly bound systems.
• Accurate PES calculation is often limited by DFT's exchange-correlation functionals and computationally intensive high-level methods.

Purpose of the Study:

• To develop a more computationally efficient DFT approach for describing Potential Energy Surfaces (PES).
• To accurately model the PES of the H2O2-Kr system using an optimized DFT method.
• To compare the performance of the novel DFT approach against established high-level computational methods.

Main Methods:

• Utilized Density Functional Theory (DFT) for in silico studies.
• Optimized a long-range parameter (omega value) within specific DFT functionals.
• Calculated the Potential Energy Surface (PES) for the H2O2-Kr system.
• Compared DFT results with Møller-Plesset perturbation theory (MPn) and Coupled Cluster Theory (CCSD(T)) benchmarks.

Main Results:

• The optimized DFT approach accurately describes the PES of the H2O2-Kr system.
• Achieved results comparable in accuracy to MPn methods, specifically MP4.
• Demonstrated a reduction in computational time compared to traditional high-level quantum chemistry methods.
• The optimized DFT method provides a viable alternative for studying weakly bound systems.

Conclusions:

• Optimizing DFT functionals, particularly the long-range parameter, significantly enhances PES calculation accuracy.
• This novel DFT approach offers a computationally efficient alternative to high-level methods for systems like H2O2-Kr.
• The findings pave the way for more accessible and faster studies of complex molecular interactions and dynamics.