One of the most widely adopted methods is Density Functional Theory (DFT), which has emerged as a powerful alternative for the quantum mechanical computation of chemical properties. As noted by George Fitzgerald and Jan Andzelm, DFT offers a more sophisticated framework for calculating molecular properties by incorporating electron correlation effects through exchange-correlation functionals . This makes DFT particularly well-suited for systems where HF theory falls short, as it provides a more accurate representation of the electron density and a more efficient computational strategy, especially for larger systems. Roberto Car and Michele Parrinello's groundbreaking work in DFT-based molecular dynamics further demonstrated the versatility of DFT in applications requiring large-scale electronic structure calculations and extensive sampling of molecular geometries, particularly in materials science and condensed matter physics . Their work showed that DFT can handle both small molecules and complex materials, offering a balance between computational cost and accuracy that is difficult to achieve with post-HF methods like configuration interaction (CI) or coupled cluster theory (CC). More recently, advances in DFT have included the development of hybrid functionals, which combine aspects of HF theory with DFT to better account for electron exchange and correlation. Studies by Scuseria et al. and the ongoing work in the field of meta-GGA functionals highlight how modern DFT approaches have narrowed the gap between computational efficiency and accuracy. These methods are increasingly being used to predict reaction mechanisms, transition states, and excited-state properties in ways that HF cannot match. Additionally, DFT has proven highly effective in the study of materials involving strong correlation effects, such as superconductors, magnetic materials, and high-pressure phases of matter, where HF-based approaches struggle. New developments in time-dependent DFT (TDDFT) and beyond-DFT methods, such as DFT+U or GW approximations, are providing even more accurate descriptions of complex systems, as highlighted in the recent review by Burke and coworkers . These advancements have extended the applicability of DFT in quantum chemistry and condensed matter physics, pushing the boundaries of what can be computed with reasonable resources. Ultimately, while HF theory laid the foundation for computational quantum chemistry, its limitations necessitate the use of more sophisticated methods like DFT for a broader range of applications. The integration of DFT into modern computational chemistry continues to yield more reliable and precise insights, particularly in the study of complex systems characterized by strong electron correlation effects. As computational methods evolve, DFT's role will likely expand, incorporating new advancements in functional development and computational algorithms, further enhancing its accuracy and efficiency in predicting chemical properties.