2019 Current Chemistry IV

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Chemistry
Instructor(s)
Juhasz Gergely 
Course component(s)
Lecture
Day/Period(Room No.)
Intensive ()  
Group
-
Course number
CHM.A438
Credits
1
Academic year
2019
Offered quarter
3-4Q
Syllabus updated
2019/9/11
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

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.

Student learning outcomes

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.

Keywords

Physical chemistry, Inorganic-analytical chemistry, Organic chemistry

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- -

Class flow

To be specified in the class.

Course schedule/Required learning

  Course schedule Required learning
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

Textbook(s)

Specified in the lecture

Reference books, course materials, etc.

Specified in the lecture

Assessment criteria and methods

Specified in the lecture

Related courses

  • CHM.C401 : Basic Concepts of Physical Chemistry
  • CHM.B401 : Basic Concepts of Inorganic Chemistry
  • CHM.D401 : Basic Concepts of Organic Chemistry

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Not specified.

Page Top