LEARNING OUTCOMES
After this course the students will
know the basics of Langevin and overdamped equation of motions,
be able to obtain and interpret radial distribution functions, direct correlation functions, and know their role in the Ornstein-Zernike equation,
be used to the concept of coarse-graining and the framework of classical density functional theory,
know the basics of Monte Carlo simulations and (pseudo) random number generators,
be able to perform simple Brownian dynamics simulations and interpret phase behaviours in ultra-soft systems,
be able to grasp recent advances in topic related to transport, phase separation and self assembly from scientific publications.
Credits: 5
Schedule: 01.11.2021 - 10.12.2021
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Alberto Scacchi, Jenny Thors
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Theoretical
1. Equations of motions (Langevin and Brownian dynamics)
2. Liquid state theory: correlation functions such as RDF, static structure factor, dispersion relation and inversions, Ornstein-Zernike equation
3. Coarse-graining concept and average density
4. Introduction to classical density functional theoryNumerical
1. Recap of Monte Carlo simulations
2. Brownian dynamics (extend to active Brownian dynamics with biological applications)
3. Classical density functional theory (numerical aspects)Phenomena
1. Cluster crystals in Generalised Exponential Models (GEM-n), a coarse grained model for dendrimers
2. Self-assembly, e.g. micelles and quasicrystals
3. Phase separations, e.g. wetting, drying and liquid-liquid phase separationProjects/Exercises
These projects require a short report which will be evaluated (marks from 0-5)
1. Introduction to random number generator in Python: use Monte Carlo to approximate the value of pi. Studyerror as a function of sample size.
2. From a given Python code simulate N Lennard-Jones particles in a 3D periodic box using Brownian dynamics simulations. Implement and obtain the radial distribution function.
3. From a given Python code simulate M GEM-8 particles in 2D periodic box using Brownian dynamics simulations. Study the cluster crystal formation for different concentrations (phase diagram).
Assessment Methods and Criteria
valid for whole curriculum period:
Oral exam (70%) and projects marks (30%) for the evaluation of the course.
Workload
valid for whole curriculum period:
36h lectures, 16h projects, 72h of personal study and 12h of scientific papers reading.
DETAILS
Study Material
valid for whole curriculum period:
Lecture notes will be provided. The theoretical part is based on the book Theory of simple liquids, J.-P. Hansen and I. R. McDonald, third Ed. (2013) and the report Effective interactions in soft condensed matter physics, C. N. Likos, Physics Reports (2001). For numerical topics and phenomena discussions the lectures will be mainly based on published manuscripts.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
12 Responsible Production and Consumption
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2021-2022: period II
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CHEM-EV02
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
SISU