Computational chemistry
Graduate · Chemistry
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Advanced quantum chemistry
- Coupled cluster theory: CCSD, CCSD(T), and convergence
- Multireference methods: CASSCF, CASPT2, NEVPT2
- Equation-of-motion CC for excited states
- Multiconfigurational perturbation theory
- Relativistic Hamiltonians and spin-orbit coupling
- Basis set extrapolation and complete basis set limits
- Composite methods: Gn, CBS-QB3 families
- Dispersion corrections: DFT-D, many-body dispersion
- Dual-level and ONIOM hybrid methods
- Benchmarking and method selection for chemical problems
Molecular dynamics and Monte Carlo
- Force field development and parameterization
- Enhanced sampling: metadynamics, umbrella sampling, REMD
- Free energy calculations: FEP, TI, BAR, MBAR
- Alchemical transformations and λ-dynamics
- Coarse-grained and multiscale modeling
- Reactive force fields: ReaxFF
- Ab initio molecular dynamics (AIMD)
- Path integral MD for nuclear quantum effects
- Analysis of MD trajectories: RDF, MSD, hydrogen bonds
- Validation against experimental observables
Cheminformatics and machine learning
- Molecular descriptors and fingerprints
- QSAR/QSPR model building and validation
- Pharmacophore modeling and 3D-QSAR
- Virtual screening pipelines and docking scoring
- Generative models for molecular design
- Neural network potentials and ML force fields
- Active learning for computational screening
- Database curation: PubChem, ChEMBL, ZINC
- High-throughput workflow automation
- Reproducibility and FAIR data in computational chemistry
Specialized applications
- Catalyst design with computational screening
- Battery electrolyte property prediction
- Protein–ligand binding free energy calculations
- Spectroscopic property prediction at high level
- Reaction network analysis and microkinetic modeling
- Materials defect calculations with periodic DFT
- Excited-state dynamics and nonadiabatic coupling
- Solvation free energies and pKa prediction
- High-performance computing and GPU acceleration
- Cloud computing for distributed simulations
STEM / applied
Software development and workflows
- Python ecosystems: RDKit, ASE, cclib, OpenMM, Psi4
- Workflow managers: Snakemake, FireWorks, Apache Airflow
- Containerization: Docker and Singularity for reproducibility
- Version control and collaborative code development
- Automated benchmarking and regression testing
- Visualization: VMD, PyMOL, NGLview, custom dashboards
- Cluster job scheduling: SLURM, PBS, cloud instances
- Documentation and teaching computational methods
- Open-source contribution and community standards
- Ethics of AI-generated molecular designs
Applied computational chemistry
- Pharmaceutical computational chemistry teams
- Materials informatics in industry R&D
- Contract computational chemistry services
- National supercomputing facility proposals (XSEDE, INCITE)
- Intellectual property for in silico discoveries
- Regulatory acceptance of computational predictions
- Consulting for litigation and patent disputes
- Startup ventures in AI-driven drug discovery
- Academic core facility management
- Career paths in computational chemistry across sectors
Notes
Graduate-level computational chemistry. Topics reflect common computational chemistry syllabi at US research universities. Programming proficiency and prior quantum chemistry coursework expected.