STEM / applied
Computational chemistry · Undergraduate · Chemistry
Learning objectives from the Computational chemistry syllabus, grouped by unit. Click an objective for study materials.
Learning objectives
Click an objective for study materials.
Foundations of computational chemistry
- Potential energy surfaces and stationary points — Potential energy surfaces and stationary points
- Molecular mechanics: force fields and parameterization — Molecular mechanics: force fields and parameterization
- Energy minimization algorithms: steepest descent, conjugate gradient — Energy minimization algorithms: steepest descent, conjugate gradient
- Conformational searching: systematic and stochastic methods — Conformational searching: systematic and stochastic methods
- Molecular dynamics: equations of motion and integrators — Molecular dynamics: equations of motion and integrators
Quantum chemistry methods
- Variational principle and SCF convergence — Variational principle and SCF convergence
- Kohn–Sham density functional theory (DFT) — Kohn–Sham density functional theory (DFT)
- Common functionals: B3LYP, PBE, M06 (overview) — Common functionals: B3LYP, PBE, M06 (overview)
- Geometry optimization and frequency calculations — Geometry optimization and frequency calculations
- Transition state search and IRC pathways — Transition state search and IRC pathways
Molecular modeling applications
- Conformational analysis of organic molecules — Conformational analysis of organic molecules
- Protein–ligand docking (introduction) — Protein–ligand docking (introduction)
- Homology modeling and structural bioinformatics (overview) — Homology modeling and structural bioinformatics (overview)
- QM/MM hybrid methods for large systems — QM/MM hybrid methods for large systems
- Reaction pathway analysis with DFT — Reaction pathway analysis with DFT
Simulation and statistical mechanics
- Monte Carlo methods: Metropolis algorithm — Monte Carlo methods: Metropolis algorithm
- Free energy calculations: FEP, TI (introduction) — Free energy calculations: FEP, TI (introduction)
- Coarse-grained models for biomolecules — Coarse-grained models for biomolecules
- Replica exchange and enhanced sampling (overview) — Replica exchange and enhanced sampling (overview)
- Radial distribution functions from MD trajectories — Radial distribution functions from MD trajectories
What each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Foundations of computational chemistry
Potential energy surfaces and stationary points
Coming soon - Quantum chemistry methods
Variational principle and SCF convergence
Coming soon - Molecular modeling applications
Conformational analysis of organic molecules
Coming soon - Simulation and statistical mechanics
Monte Carlo methods: Metropolis algorithm
Coming soon
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$1,162 · Computational chemistry · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.