Simulation of Condensed Matter and Biosystems
A.Y. 2023/2024
Learning objectives
The course will teach the techniques and the algorithms to study kinetic and equilibrium properties of classical models of molecules, polymers, and solids.
Expected learning outcomes
The student at the end of the course will have the following abilities:
1. Understand the theory that controls molecular dynamics simulations
2. To perform molecular dynamics simulations
3. To understand the algorithms to calculate free energies
4. To be able to deal with data analysis through simple scripts
1. Understand the theory that controls molecular dynamics simulations
2. To perform molecular dynamics simulations
3. To understand the algorithms to calculate free energies
4. To be able to deal with data analysis through simple scripts
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
1) Molecular dynamics of molecules and solids in the microcanonical ensemble, integrators, ergodicity and integrability, temperature
2) molecular dynamics at fixed temperature, Langevin dynamics, calculation of thermodynamic properties, phase transitions, thermostats and barostats, optimization algorithms
3) Thermodynamic sampling, Metropolis algorithm, glassy transitions, tempering and multicanonical methods.
4) Molecular dynamics in explicit solvent, the electrostatic problem, semi empirical potentials, reaction coordinate, umbrella sampling and meta dynamics.
2) molecular dynamics at fixed temperature, Langevin dynamics, calculation of thermodynamic properties, phase transitions, thermostats and barostats, optimization algorithms
3) Thermodynamic sampling, Metropolis algorithm, glassy transitions, tempering and multicanonical methods.
4) Molecular dynamics in explicit solvent, the electrostatic problem, semi empirical potentials, reaction coordinate, umbrella sampling and meta dynamics.
Prerequisites for admission
Basic knowledge of Linux and of the C language
Teaching methods
Lectures and exercises on computers
Teaching Resources
Notes that can be downloaded from the Ariel site.
Assessment methods and Criteria
Oral examination to assess the degree of comprehension of the theoretical aspects of the theory, of the ability to reproduce the calculations discussed during the lectures, to implement the algorithms, of critical thinking and to connect to the subjects learned in other courses.
Educational website(s)
Professor(s)