K-Feldspar IN MD, photo by O. Pakarinen and T. Ponkkonen

MATR325 Molecular Dynamics Simulations

The course is about Molecular Dynamics (MD), a widely used computational tool in physics / chemistry / bioscience.

During the course we will discuss the theoretical basis and algorithms for MD, learn analysis methods and visualization, and apply this knowledge in practice to solve atomic scale problems in physics and related fields of science with hands-on approach.
See Moodle page (link below) for more information.

14.8.2017 klo 09:00 - 12.12.2017 klo 23:59


Tästä osiosta löydät kurssin opetusaikataulun. Tarkista mahdolliset muut aikataulut kuvauksesta.

Ma 4.9.2017
10:15 - 12:00
Ma 11.9.2017
10:15 - 12:00
Ma 18.9.2017
10:15 - 12:00
Ma 25.9.2017
10:15 - 12:00
Ma 2.10.2017
10:15 - 12:00
Ma 9.10.2017
10:15 - 12:00
Ma 16.10.2017
10:15 - 12:00
Ma 30.10.2017
10:15 - 12:00
Ma 6.11.2017
10:15 - 12:00
Ma 13.11.2017
10:15 - 12:00
Ma 20.11.2017
10:15 - 12:00
Ma 27.11.2017
10:15 - 12:00
Ma 4.12.2017
10:15 - 12:00
Ma 11.12.2017
10:15 - 12:00


Master's Programme in Materials Research is responsible for the course.

Modules where the course belongs to:

  • MATR300 Advanced Studies in Materials Research
    Optional for:
    1. Study Track in Computational Materials Physics
  • TCM300 Advanced Studies in Theoretical and Computational Methods

The course is available to students from other degree programmes.

  • Basics of mechanics and structure of matter and molecules
  • Thermodynamics at the level of course FYS2001 Basics of Thermophysics
  • Programming skills (file read/write, calculus) to independently solve small programming tasks. Programming will not be taught on the course.
  • Familiarity with the Linux programming environment is strongly suggested.
  • Knowledge of solid state physics on the level of course MATR303 Solid State Physics I is recommended.
  • Knowledge of quantum mechanics on the level of course FYS2018 Quantum Mechanics I is recommended.

Course MATR327 Computational Nanoscience develops further the skills in applying molecular dynamics in nanoscale simulations.

After successful completion of the course you will

  • understand the theoretical basis of MD simulations
  • know typical application areas, and can assess the suitability of the method to a given problem
  • can independently design, run and analyse a simulation in a relatively simple scientific problem
  • understand the significance of basic algorithms and their parameters for a successful simulation
  • understand the method well enough to be able to deepen your knowledge of details independently
  • also learn independent project work and solving open real-world problems

Can be taken either in the first or second year of studies.

Given every second year (odd years), autumn term (periods I & II).

  • Application areas of molecular dynamics in physics, chemistry and biophysics
  • Theoretical basis and algorithms for simulations
    • Sampling of phase space and solving MD algorithms piece by piece
    • Geometry optimization
    • Integrating equations of motion
    • Calculating interactions for different types of systems
    • Basics of Ab Initio MD
    • Taking system temperature and pressure into account
  • Applying this knowledge to solve problems in practice
  • Analyzing the results
  • Visualization at the atomic level

    Lecture notes.

    • Supplementary reading
      • Allen and Tildesley: Computer Simulation of Liquids
      • Andrew R. Leach: Molecular Modelling. Principles and Applications
      • Frenkel and Smit: Understanding Molecular Simulation
      • M. Tuckerman: Statistical mechanics: Theory and Molecular Simulation
      • Marx and Hütter, Ab Initio Molecular Dynamics

    Weekly lectures and exercises (individual work). Three computational projects (individual). Final exam. Total hours 250.

    Final grade is based on exercises (25 %), exam (25 %) and three projects (50%).

    Exercises, projects and exam (all three count into completion).

    Exercises are mostly small simulation tasks and sometimes theoretical ones. Projects are larger, more open research tasks with reports.