Duration
26h Th, 14h Pr
Number of credits
| Master MSc. in Engineering Physics, research focus | 4 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The course aims at providing the theoretical foundations necessary to bridge the conceptual gap between the (quantum) microscopic atomic and molecular description of matter and the macroscopic, experimentally observable, properties in systems at thermodynamical equilibrium. To achieve this, we will make use both theory and numerical simulations. Within the different course units, we will cover the following topics:
- Fundamentals on Statistical Mechanics
- Common statistical ensembles: from partition functions to Thermodynamics.
- Fundamentals Molecular Dynamics and Monte Carlo sampling techniques.
- Introduction to molecular quantum mechanics.
- Ab initio computation of thermodynamical molecular properties.
- Statistical mechanics of systems of indistinguishable particles.
- Simulation of systems of interacting particles: from real gases to chemical reactivity.
Learning outcomes of the learning unit
Students are expected to demonstrate a comprehensive understanding of core assumptions in statistical mechanics, the concept of phase space and the ergodic hypothesis, and the derivation of thermodynamic functions from microcanonical, canonical, and grand canonical partition functions. They must be capable of treating quantum mechanical systems of indistinguishable particles using Fermi-Dirac and Bose-Einstein statistics. From a computational and simulation perspective, students should be able to apply the principles of phase-space sampling using classical Molecular Dynamics (integrators, ensembles) and Metropolis Monte Carlo methods. Furthermore, students are expected to understand the fundamentals of ab initio molecular quantum chemical calculations (self-consistent methods) necessary to construct molecular potentials and, in turn, compute molecular partition functions.
Prerequisite knowledge and skills
Students should master the fundamentals of classical thermodynamics and quantum mechanics. Prior exposure to basic programming is beneficial.
Planned learning activities and teaching methods
The Physical Chemistry course combines lectures, exercise sessions focused on applying theoretical concepts, and practical work featuring numerical simulations using Jupyter Notebooks (Python) and molecular electronic structure software.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
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Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Exam(s) in session
Any session
- In-person
written exam
Further information:
The final course grade will be determined entirely by the exam, graded out of 20.
Work placement(s)
Organisational remarks and main changes to the course
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Contacts
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