Computational Materials Science Field of study: Biophysics
Programme code: W4-S2BFA21.2022

Module name: Computational Materials Science
Module code: W4-2BF-MB-21-34
Programme code: W4-S2BFA21.2022
Semester:
  • winter semester 2024/2025
  • winter semester 2023/2024
  • winter semester 2022/2023
Language of instruction: English
Form of verification: exam
ECTS credits: 6
Description:
Intermolecular Forces: Hydrogen bonding, Electrostatic interactions, London forces. Molecular clusters, Supramolecular assemblies. Thermodynamics: Variational formulation. Free energy of a reaction, Equilibrium constants. Statistical Mechanics: Gibbs ensemble, Mechanical system, Generalized coordinates, Lagrangian formalism. Hamiltonian formalism, Hamilton's equation, Phase space. Properties of Hamiltonian systems, Conservation laws, Canonical transformation, Poisson brackets, Liouville's operator, Equation of motion of dynamical variables. Liouville's equation and theorem, Probability density, Microcanonical ensemble, Canonical ensemble. Molecular dynamics: Definition, Foundations of molecular simulations, Limits and approximations. Overview of the basic ingredients (Energy potential, Force fields, Numerical integrators). Energy potential, Force fields, Numerical integrators. Force field terms (bonding, bending, torsion, non-covalent interactions). Molecular Dynamics: Coordinate and Velocity initialization, Integrators. Numerical integrators (velocity Verlet, Leapfrog), Statistical mechanical ensemble, Thermal and pressure coupling. Enhanced Sampling Methods. Simulation of the Kv ion channel. Simulation of a lipid bilayer. Fundamentals of enhanced sampling techniques. Implicit solvent and continuum electrostatic modeling. From collisional theory to stochastic dynamical systems. Stochastic differential equations and Statistical Mechanics. Structural properties: distribution function, radial distribution functions. Monte Carlo methods: Numerical Integration, Importance sampling. Free Energy methods. Free Energy Methods: Thermodynamic Integration, Free energy perturbation, Umbrella Sampling Free Energy Methods: Metadynamics, Jarzinski method, Adiabatic free energy. The course aims to provide an overview of the theories and methodologies currently used in various fields of computational molecular sciences, ranging from biomedical sciences to material sciences. A special focus will be devoted to those models and algorithms related to molecular simulation techniques, including enhanced sampling and free energy methods. Such models will be illustrated along with relevant examples taken from recent literature and concerning different molecular modeling applications.
Prerequisites:
(no information given)
Key reading:
(no information given)
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
the student can comprehend molecular modeling techniques currently used in the field of life and material sciences [MB_34_1]
 KBF_W02 [4/5]
The student develops competencies on some of the most common computational methodologies used in molecular sciences [MB_34_2]
 KBF_W03 [4/5] KBF_W08 [4/5]
the student develop computational skills through tutorials and exercises [MB_34_3]
 KBF_K04 [4/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
exam [MB_34_w_1]
Oral Exam. In addition to questions related to the basic knowledge of the course, students will be asked to present a scientific problem of their interest suitable to be treated with molecular modeling methodologies.
 MB_34_1 MB_34_2 MB_34_3
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [MB_34_fs_1]
detailed discussion by the lecturer of the issues listed in the table "module description" using the table and/or multimedia presentations
 48
supplementary reading, working with the textbook
 102 exam [MB_34_w_1]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)