Vorlesung mit Übung und Praktikum (Master), 3+1+3 SWS

Studiengänge: Chemie- und Bioingenieurwesen, Chemical Engineering – Nachhaltige Chemische Technologien

Inhalt: Die Lehrveranstaltung befasst sich mit der Simulation von Systemen vieler Teilchen mit Hilfe verschiedener numerischen Methoden:

• Molekulardynamik (zeit- und ereignisgesteuert), Monte Carlo
• Statistik, Observablen und Trajektorien
• Methoden des Coarse-graining
• Diskrete-Element Methode (DEM) zur Simulation von granularen Systemen
• Starrkörpersimulation als Alternative zu DEM

Lernziele und Kompetenzen: Die Studierenden

• sind mit den grundsätzlichen Methoden der numerischen Modellierung molekularer und granularer Systeme vertraut
• besitzen vertiefte Kenntnisse bezüglich der verwendeten numerischen Methoden und der wichtigsten Algorithmen und Datenstrukturen
• verstehen die Anwendung interatomarer und intermolekularer Wechselwirkungen und deren rechnerische Umsetzung
• implementieren einzelne Aspekte dieser Methoden
• modellieren einfache Systeme
• können selbständig numerische Simulationen dieser Systeme durchführen und auswerten (Praktikum)

Vorlesung (Master), 2 SWS

Studiengänge: Advanced Materials and Processes, Computational Engineering

Inhalt: Introduction to scientific computing as well as the basic concepts of particle-based modelling and simulation in materials science and process technology. The lectures provide an overview of different techniques, methods, and applications thereof from the atomic scale via the mesoscale to the microscale:

• Overview of different simulation methods on the atomic, micro-, and mesoscale
• Statistics, observables, and trajectories
• Particle-based coarse-graining
• Process modeling with population balance

Lernziele und Kompetenzen: Students who successfully participate in this module can:

• explain basic techniques and methods of numerical modelling of particulate systems on various scales from atoms to molecules to granular matter
• describe limitations and strengths of common simulation algorithms and data structures
• understand the use of interatomic and intermolecular interactions and their computational implementation

Vorlesung mit Übungen (Bachelor), 2+2 SWS

Studiengänge: Clean Energy Processes

Inhalt: Students who participate in this course will become familiar with basic concepts of Python programming, data science and statistics:

• Introduction to Python
• Basic data types and data structures
• Scientific algorithms
• Visualizing data
• Probability and statistics
• Hypothesis testing and interference
• Optimization: Gradient descent, deterministic and stochastic
• Supervised learning: classification, regression
• Clustering

Lernziele und Kompetenzen: Students who successfully participate in this module can:

• Understand and write scientific code in Python
• Utilize good programming practices and describe algorithms
• Select appropriate data structures and process data with them
• Apply the concept of probability distributions
• Perform elementary statistical analysis
• Extract information from data sets
• Make predictions, quantify their accuracy and reliability
• Apply all of the concepts above to selected engineering problems

Hauptseminar (Bachelor/Master), 2 SWS

Studiengang: Physik

Inhalt: In the seminar, journal papers from the field of soft matter physics are discussed. This is a joint seminar offered by the Institute for Theoretical Physics 1 (NatFak), the Institute for Multiscale Simulation (TechFak), and the Chair of Mathematics in Life Sciences (NatFak).

Vorlesung mit Übung (Master), 2+1 SWS

Studiengänge: Chemie- und Bioingenieurwesen, Chemical Engineering – Nachhaltige Chemische Technologien, Life Science Engineering

Inhalt: Structure formation with elementary building blocks in molecular, particulate, soft, and biological systems. Theoretical aspects, experimental realizations, and applications are discussed.

• Theory 1 (introduction): the idea of building blocks, thermodynamic principles
• Theory 2 (continuum): spinodal decomposition, reaction diffusion, phase field model, feedback
• Theory 3 (particles): entropy maximization, interface minimization
• Molecules 1 (basics): molecular interactions, role of shape
• Molecules 2 (liquid crystals): topological order, defects
• Molecules 3 (interfaces): surfactants, micelles, emulsions, foams, vesicles
• Molecules 4 (beyond): block copolymers, membranes, proteins, metal organic frameworks
• Colloids 1 (isotropic particles): interaction forces, depletion, hydrophobic(-philic)
• Colloids 2 (directed assembly): diffusion-limited aggregation, printing, coffee stain effect
• Colloids 3 (anisotropic particles): nanoparticles, patchy particles, ligands, directionality
• Colloids 4 (properties): surface functionalization, plasmonics, filtration, catalysis, mechanical
• Bioinspired 1 (dynamic self-assembly): active matter, bacteria, swarms, robots
• Bioinspired 2 (design): programmable assembly, DNA nanotechnology, inverse problems

Lernziele und Kompetenzen: The students

• describe complex self-organization processes with the help of simple model systems
• apply this knowledge to physical, chemical, and bioinspired systems
• develop an advanced understanding of the self-organization of (macro)molecules and colloids
• understand processes to direct and influence self-organization processes
• judge the relevance of self-organization for the processing and synthesis of materials
• gain insight into current research in the field of the lecture

Hauptseminar (Bachelor/Master), 2 SWS

Studiengang: Physik

Inhalt: In the seminar, journal papers from the field of soft matter physics are discussed. This is a joint seminar offered by the Institute for Theoretical Physics 1 (NatFak), the Institute for Multiscale Simulation (TechFak), and the Chair of Mathematics in Life Sciences (NatFak).