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)