**Teaching activities in the summer term 2022:**

**05-MCM-CM COMPUTATIONAL MATERIALS SCIENCE**

*Learning Contents:*

The most important computational methods for the quantum mechanical modeling of materials in the electronic ground state will be discussed in detail. These methods will be applied in a practical course towards the end of the semester, after the lecture block is finished. The following aspects will be treated in the module: Hartree-Fock theory; electron correlation and post-Hartree-Fock methods; Density Functional Theory; Basis sets (Gaussian and plane-wave); calculations with periodic boundary conditions

*Learning Outcomes, Targeted Competencies:*

The students will have an understanding of the state-of-the-art computational methods in materials chemistry and mineralogy and will…

…be able to assess the reliability of a given computational method in the description of an experiment, e.g. when reading literature

…be able to devise basic computational protocols to calculate a desired property

…have first experiences in the usage of quantum mechanical program packages

*Prior Knowledge:*

Basic knowledge of quantum mechanics and molecular orbital theory

Course Type 1: Lecture (L) 3.0 SWS ( 42.0 h)

Course Type 2: Practical Laboratory (LP) 1.0 SWS ( 14.0 h)

Tutorial(s): –

*Workload:*

56.0 h presence time

84.0 h self-study

40.0 h exam workload

180 h total workload

*Exam Type:*

combination exam

*Examination:*

exam elements: 2

SL: 0

70 % oral exam

30 % internship report

*Literature:*

Cramer: Essentials of Computational Chemistry

Szabo/Ostlund: Modern Quantum Chemistry

Parr/Yang: Density Functional Theory of Atoms and Molecules

Martin: Electronic Structure: Basic Theory and Practical Methods

**Data Processing and Plotting in Chemistry**

The course will cover lectures about basics in Python programming together with hands-on programming sessions.

• Overview of programming languages and plotting tools

• Structural/ scientific data bases

• Basics of Python programming: variables, lists, arrays; basic math; read/write; Functions/classes

• Processing of larger data sets from organic, inorganic, and physical chemistry

• Visualisation with Matplotlib

• Graphical visualisation and interpretaion of experimental data (e.g. IR or NMR spectra, XRD data, quantum chemical calculations)

The aim of the course is to learn how to process typical scientific data from chemical experiments and simulations. The students learn how to process their data with basic mathematical operations and how to plot and analyze the data at the level of scientific publishing. For this purpose the students will learn the basics of the programming language Python and how to plot data using Matplotlib.

More information will follow soon after in cooparat

*Prior Knowledge:*

Basic knowledge of chemical experiments; no programming knowledge needed!

Course Type 1: Lecture (L) 2.0 SWS ( 28.0 h)

Course Type 2: Practical Laboratory (LP) 2.0 SWS ( 28.0 h)

Tutorial(s): –

*Workload:*

56.0 h presence time

84.0 h self-study

40.0 h exam workload

180 h total workload

*Exam Type:*

combination exam

*Examination:*

exam elements: 2

SL: 0

50 % oral exam

50 % internship report

**Mini Lectures**

A mini lecture on the Pauli Principle (german slides)