This research presents a comparative study of Hartree-Fock (HF) and DensityFunctional Theory (DFT) methods,
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evaluating their computational efficiency
and accuracy across multiple atomic systems. The study benchmarks orbital energy
predictions, SCF convergence iterations, and total energy assessments across
advanced quantum chemistry frameworks. Furthermore, Coupled-Cluster (CCSD
and CCSD(T)) methodologies are assessed against HF and DFT to highlight performance
trade-offs. The integration of machine learning (ML) and
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