Application deadline

Applicants from Nordic countries: 1 June for the autumn semester

Application code 9303 in Søknadsweb.

Exchange students and Fulbright students: 15 April for the autumn semester.

Type of course

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. Valid documentation is a statement from your institution that you are a registered PhD student, or a Master´s Diploma with Diploma Supplement / English translation of the diploma. PhD students are exempt from semester fee. PhD students at UiT The Arctic University of Norway register for the course through StudentWeb. External applicants apply for admission through SøknadsWeb. Application code 9303.All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

The course requires basic calculus knowledge (functions, integrals and derivatives in one and more variables, linear algebra, complex numbers). Basic knowledge in physics (elementary classical mechanics and electromagnetism) is an advantage.

Course content

Computers are nowadays ubiquitous in any chemistry lab. Not only in assisting other more traditional instruments but also as tools in their own right: even small workstations have become so powerful that quantum modeling of molecules, their structure, properties and behavior can be conveniently carried out on a desktop machine. In addition, most Universities and research facilities offer a High Performance Computing platform where more demanding tasks can be performed. Mastering computational chemistry methods must nowadays be regarded as important as modern spectroscopic techniques.

The goal of the present course is to present the methods of quantum chemistry in a hands-on fashion in order to enable PhD students to make proficient use of them in their PhD research activity and subsequently in their professional activity.

The course will start by presenting a general overview of quantum molecular modeling and its current use in chemistry. We will briefly touch upon classical modeling and molecular mechanics. We will then introduce wave function theory which is at the foundation of Quantum Chemistry. The main wave function methods will be presented, highlighting their strengths and weaknesses in connection to their practical use. We will also introduce Density Functional Theory (DFT) which is at present the most widely employed method in quantum chemistry. Optimization methods will be discussed in connection both with wave function theory and DFT to find the ¿optimal¿ wave function and also in relation to geometric problems such as finding the structure of a molecule or a transition state of a reaction. The computation of molecular properties which leads e.g. to the modeling and interpretation of spectroscopic data will also be presented. We will also describe how to use computational results in order to obtain thermodynamic quantities such as the enthalpy or the free energy of a reaction. As most of chemistry happens in condensed phase, we will dedicate the last part of the course to the methods to deal with the effect of the solvent on molecules and the techniques (both implicit and explicit) to include such a solvent effect in the calculations.

All lectures will be followed by computational exercises where the students will be able to use their acquired knowledge on illustrative examples. 

Objectives of the course

Knowledge:

The candidate

• will be able to explain the main features of a force field used in molecular mechanics, its use and its origin.
• will learn the fundamental properties of wave function methods
• will be able to describe and classify wave function methods, pointing out their strengths, weaknesses and applicability.
• will understand the foundation of Density Functional Theory, based on the Hönberg-Kohn theorems and the Kohn-Sham equations.
• will be able to describe the features of the main classes of density functionals, their development and their applicability.
• will learn the main optimization methods and their use in connection to wave function/density minimization and geometry optimization (minima and saddle points).
• will learn how to make use of computational methods to acquire information and make predictions about a chemical system: structure, spectroscopic and thermodynamic properties, reactivity.

Skills:

The candidate

• will be able to set up and run single point calculations and check the outcome in terms of achieved convergence and meaning of the result.
• will be able to identify the molecular orbitals and explain their meaning, in particular for frontier orbitals (HOMO/LUMO).
• will be able to run multilevel geometry optimizations and analyze the convergence of the result.
• will be able to assign the bands of an IR spectrum based on the result of a QM calculation.
• will be able to evaluate the convergence of a calculation with respect to the basis set employed.
• will make use of response theory to compute static and frequency-dependent polarizabilities.
• will be able to explain the solvent effect on molecular structure and properties.
• will make use of a programming language of choice (Fortran, C, Python) to compute overlap integrals between basis functions.
• will identify and characterize a reaction path from the reactants through the transition state to the products.
• will compute magnetic properties of molecules such as the NMR shielding constants and the magnetizabilities.

General competence:

The candidate will be able to...

• employ computational methods in her/his PhD research project.
• independently design and carry out the best computational strategy in order to investigate the problem at hand.
• interpret and explain the outcome of a computation.
• evaluate the quality and the reliability of the obtained results.
• write a report which describes the work done and analyzes the results.

Language of instruction and examination

Teaching methods

Assessment

A final oral exam/presentation. Passed/not passed.

Coursework requirements:

• Compulsory attendance in computational exercises. 
• For each exercise the student will have to write a report explaining the theoretical background, the computational strategy adopted and the obtained results. The reports will be evaluated and graded passed/not passed.

The candidates with a positive report evaluation (passed) will be given two research articles, which they will have to present to the evaluating committee, discussing the methods used and the results in light of the knowledge acquired during the course.

Candidates that don't pass the exam can repeat the exam the following year. If they don't pass the report evaluation, they shall submit an improved version of the reports for approval, prior to the oral exam.

Alternatively, the entire course can be followed again.