This dissertation advances the field of density functional theory (DFT) by evaluating the performance of modern density functionals in predicting a variety of chemical properties and by developing new functionals with improved accuracy and transferability. The work begins with the derivation and efficient implementation of the second analytical derivative of the VV10 nonlocal correlation (NLC) functional, enabling the calculation of second-order properties---such as harmonic vibrational frequencies and excitation energies---with VV10-containing functionals without significant computational overhead.
Building on this foundation, the performance of VV10-containing functionals is benchmarked for harmonic vibrational frequencies. While the VV10 contribution is minimal for small covalent molecules, it proves significant for systems where weak interactions are critical, such as water clusters. Scaling factors are provided to convert harmonic frequencies to fundamental frequencies and zero-point vibrational energies for several modern functionals, facilitating more accurate infrared spectroscopy predictions.
The study further evaluates over 40 popular and recently developed density functionals for 463 vertical excitation energies using the comprehensive QuestDB benchmark set. The findings confirm that while time-dependent DFT adheres to the principles of Jacob's Ladder, hybrid meta-generalized gradient approximation (meta-GGA) functionals do not consistently outperform hybrid GGAs. Factors influencing the accuracy of excitation energy calculations---including basis set convergence, gauge invariance corrections for meta-GGAs, and the role of VV10 NLC---are thoroughly investigated.
Addressing the need for improved functional transferability, a new and extensive chemical database named GSCDB138 is developed. This database significantly expands upon existing benchmarks by incorporating underrepresented areas such as transition metals, main-group metal clusters, electric properties, vibrational frequencies, and specialized noncovalent interactions. Most reference data are updated to theoretical best estimates, enhancing the reliability of functional assessments.
Leveraging the expanded database, a new range-separated hybrid meta-GGA functional is developed using a mixed-integer optimization algorithm. This approach explores an immense functional space while enforcing multiple physical constraints to maximize accuracy without overfitting. The resulting functional exhibits exceptional performance across a wide array of chemical systems, surpassing existing functionals like $\omega$B97M-V in both accuracy and transferability.
In summary, this work provides significant contributions to computational chemistry by offering comprehensive benchmarks and advanced functionals that collectively enhance the predictive power of DFT for diverse chemical applications.