HUNTERTUTORING

Standard syllabus

Computational chemistry · Undergraduate · Chemistry

Topics

Foundations of computational chemistry

  • Potential energy surfaces and stationary points
  • Molecular mechanics: force fields and parameterization
  • Energy minimization algorithms: steepest descent, conjugate gradient
  • Conformational searching: systematic and stochastic methods
  • Molecular dynamics: equations of motion and integrators
  • Periodic boundary conditions and Ewald summation (intro)
  • Thermodynamic ensembles: NVE, NVT, NPT
  • Basis sets in quantum chemistry: STO, GTO, split-valence
  • Hartree–Fock self-consistent field method
  • Electron correlation: MP2, CI, CCSD (overview)

Quantum chemistry methods

  • Variational principle and SCF convergence
  • Kohn–Sham density functional theory (DFT)
  • Common functionals: B3LYP, PBE, M06 (overview)
  • Geometry optimization and frequency calculations
  • Transition state search and IRC pathways
  • Thermochemistry from computed energies: ZPE, enthalpy, entropy
  • Solvation models: PCM, SMD, explicit solvent
  • Basis set superposition error and counterpoise correction
  • Spin contamination and unrestricted calculations
  • Limitations and validation of computational methods

Molecular modeling applications

  • Conformational analysis of organic molecules
  • Protein–ligand docking (introduction)
  • Homology modeling and structural bioinformatics (overview)
  • QM/MM hybrid methods for large systems
  • Reaction pathway analysis with DFT
  • Spectroscopic property prediction: NMR, IR, UV-Vis
  • Intermolecular interactions: hydrogen bonding, π-stacking
  • Crystal structure prediction (overview)
  • High-throughput virtual screening
  • Visualization: molecular orbitals, electrostatic potentials, ESP maps

Simulation and statistical mechanics

  • Monte Carlo methods: Metropolis algorithm
  • Free energy calculations: FEP, TI (introduction)
  • Coarse-grained models for biomolecules
  • Replica exchange and enhanced sampling (overview)
  • Radial distribution functions from MD trajectories
  • Mean square displacement and diffusion coefficients
  • Protein folding simulations (conceptual)
  • Materials simulations: defects, surfaces, interfaces
  • Machine learning potentials (overview)
  • Reproducibility and benchmarking in computational chemistry

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