Computational Quantum Chemistry for Molecules and Materials

10 credits

Syllabus, Master's level, 1KB273

Education cycle
Second cycle
Main field(s) of study and in-depth level
Chemistry A1F, Physics A1F
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 6 October 2023
Responsible department
Department of Chemistry - Ångström

Entry requirements

120 credits with 60 credits in chemistry or physics including 4 credits completed in Chemical Bonding and Computational Chemistry. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student shall be able to:

  • analyse how quantum chemical calculations can provide unique information and understanding of properties of molecules and materials,
  • critically analyse central aspects of the quantum-chemical methods for molecules, with emphasis on static and dynamic electron correlation,
  • critically analyse central aspects of models, methods and machinery of electronic structure calculations for condensed-matter systems, surfaces and interfaces, with an emphasis on periodic calculations,
  • use some of these models and methods in practical quantum-chemical calculations, make adequate interpretation of the results,
  • account for advantages and disadvantages of the various methods discussed in the course,
  • evaluate basic principles behind some methods that combine quantum mechanics and classical force fields (such as QM/MM) to describe large chemical systems, both molecules and materials,
  • discuss the essential features of research articles in applied computational quantum chemistry,
  • discuss some of the important and timely current problems within the area of quantum chemistry methods and calculations,
  • account for some aspects of the role of machine learning techniques within modern computational chemistry and e-science, such as force-field development and Molecules/Materials property prediction.


Computational quantum chemistry can generate new information as well as deep and detailed understanding within most fields of chemistry. The course covers different electron-correlated QC methods for molecules and condensed-matter systems (solids, surfaces and nanomaterials), e.g., for energy and catalysis applications. Electronic structure "from bonds to bands". The course also provides an orientation of quantum chemistry as a building block within multiscale modelling.

The following concepts are discussed: Potential energy surfaces, electronic properties, Hartree-Fock theory (restricted and unrestricted) for molecules, DFT theory, open-shell systems, Slater determinants, static and dynamic electron correlation (CI, CC, CASSCF, MPx). Periodic DFT and Hartree-Fock calculations for the solid state and their surfaces (from a chemical perspective), plane wave basis sets for materials, DOS (density of electronic states) and quantum-chemical methods to describe long-range interactions. Non-periodic calculations for condensed matter. QM/MM methods. Calculation and interpretation of properties for molecules and materials.

A short overview of the quantum-mechanical postulates and of some of the important quantum-mechanical concepts and notation will be given at the beginning of the course. Basis of machine learning and neural networks.


Lectures, computer lab sessions, literature assignment with oral and possibly written presentations.


A written examination takes place at the end of the course (4 credits). Laboratory sessions and the literature assignment (6 credits). 

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.