Computational chemistry
Undergraduate · Chemistry
Syllabus focus
Standard syllabus · STEM / applied
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Topics typically covered
Standard syllabus
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
STEM / applied
Software and workflows
- Gaussian, ORCA, or Psi4 for quantum calculations
- GaussView/ChemCraft for building and visualizing structures
- VMD and PyMOL for biomolecular visualization
- GROMACS, AMBER, or NAMD for molecular dynamics
- Python scripting: RDKit, ASE, cclib
- Cluster computing and job submission
- Input file preparation and convergence troubleshooting
- Parsing output files for energies, geometries, frequencies
- Computational lab notebooks and version control
- Ethics of computational research and data sharing
Applied computational chemistry
- Drug design: lead optimization and ADMET prediction
- Catalyst design with DFT
- Battery electrolyte screening
- Polymer property prediction
- Atmospheric chemistry reaction rate calculations
- Materials defect energetics
- Spectral assignment assistance for experimentalists
- Patent and intellectual property in silico methods
- Industry workflows in pharmaceutical and materials R&D
- Career paths in computational chemistry and cheminformatics
Notes
Topics reflect common computational chemistry syllabi at US colleges and universities. Prior physical chemistry and programming experience are helpful. Access to computational resources varies by institution.