Computational chemistry become a powerful tool in chemical research, including the characterization and design of catalysts. The goal of this lecture is to summary the opportunities, limitations and challenges using these tools.
We review the basic theory behind DFT and force field-based approximations, and how to create models for molecular systems and surfaces. Such calculations can be helpful in identifying different chemical species, analyze their electronic structure and predict their spectroscopic properties. We briefly review these methods and how reliable they are. Using practical examples, we also discuss special challenges typical to surfaces, nanoparticles and electrocatalytic systems.
Physical chemistry, Inorganic-analytical chemistry, Organic chemistry
✔ Specialist skills | ✔ Intercultural skills | ✔ Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
To be specified in the class.
Course schedule | Required learning | |
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Class 1 | 1-2. Basics of computational chemistry Types of methods (variational and perturbation methods, HF vs DFT approximation) Quantum chemical models, basis sets, DFT functionals Scaling of calculations (method and models) Molecular orbital picture vs DFT results | Specified in the lecture |
Class 2 | 1-2. Basics of computational chemistry Types of methods (variational and perturbation methods, HF vs DFT approximation) Quantum chemical models, basis sets, DFT functionals Scaling of calculations (method and models) Molecular orbital picture vs DFT results | Specified in the lecture |
Class 3 | 3. Molecular systems and homogenous catalysis Example: Oxygen activation on iron complexes Magnetic properties: spin states, spin-orbit coupling, magnetic coupling | Specified in the lecture |
Class 4 | 4. Simulation of periodic systems Example: nanotubes, metal and metal-oxide surfaces Brillion zone and k space Band structure and DOS vs molecular orbital picture Creating periodic models: supercells, surface slab models | Specified in the lecture |
Class 5 | 5. Electrochemistry Example: metal and metal-oxide electrode surfaces Thermodynamics vs kinetics in electrochemistry, Computational H electrode | Specified in the lecture |
Class 6 | 6. Beyond regular DFT calculations Semi-empirical methods (DFTB) High-throughput methods and automation Fast molecular dynamics (ReaxFF, on-the-fly force fields), machine learning-based approaches | Specified in the lecture |
Specified in the lecture
Specified in the lecture
Specified in the lecture
Not specified.