2025/26 Undergraduate Module Catalogue

PHAS2020 Statistical Mechanics and Computation

Module manager: Charley Schaefer
Email: C.Schaefer@Leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2025/26

Pre-requisite qualifications

Level 1 physics

Pre-requisites

PHAS1000 First Year Physics Assessment

Co-requisites

PHAS2000 2nd year Physics Assessment
PHAS2010 Quantum Mechanics
PHAS2030 Condensed Matter Physics
PHAS2040 Electromagnetism

Module replaces

PHYS2300, PHYS2320

This module is not approved as a discovery module

Module summary

This module explores the concepts and applications of statistical mechanics, which are key to understanding the behaviour of small-particle systems. This module will also enable students to translate descriptions of physical problems and data analysis processes into short programs to read and manipulate data, analyse and present the results for problems relevant to physics using a programming language.

Objectives

During this module students will learn the theories and concepts of statistical mechanics. Examples and applications will be used to allow students to build their understanding and application of this branch of physics, which is fundamental to explaining the macroscopic behaviour of atoms and other small-particle systems.

This module will also introduce students to statistical analysis and levels of measurement and hypothesis testing.

Computer programming is an important skill for Physics students to learn, preparing them for both higher level academic studies and a wide range of professional careers. This module further develops students’ skills in programming and focuses on applying programming to solve realistic data analysis problems in physics. This module covers tasks such as reading data files, manipulating and fitting data to theoretical models and visualising and presenting the results. For this module we use the Python programming language as it is widely used in scientific environments, freely available and provides a rich set of libraries for carrying out data analysis.

Learning outcomes

On successful completion of the module students will be able to demonstrate knowledge, understanding and application of the following:

1- Microstates, macrostates, canonical ensembles
2- Boltzmann statistics
3- Partition functions
4- Bose-Einstein statistics
5- Fermi-Dirac statistics
6- Bose-Einstein condenstation
7- Modelling simple physics situations in computer code
8- Reading text-based data files and manipulating data in computer code
9- Fitting data to simple models
10- Suitable visualisation of data and results
11- Data analysis techniques
12- Planning and execution of a piece of work over an extended period
13- Evaluation of progress through regular reporting

Skills Learning Outcomes

On successful completion of the module students will be able to do the following:

a- Manage time and plan work to meet deadlines
b- Problem solving
c- Application of appropriate mathematics
d- Coding skills

Syllabus

1- Macrostates and Microstates
2- Boltzmann statistics for distinguishable particles
3- Partition functions
4- Two-level paramagnet
5- Statistics of indistinguishable particles
6- Fermi-Dirac statistics
7- Bose-Einstein statistics
8- Bose-Einstein condensation
9- Computer modelling of physical systems
10- Programming for data analysis
11- Data visualisation

Methods of assessment
The assessment details for this module will be provided at the start of the academic year

Teaching Methods

Delivery type Number Length hours Student hours
Supervision 10 1 10
Lecture 46 1 46
Practical 6 2 12
Practical 9 1 9
Independent online learning hours 23
Private study hours 100
Total Contact hours 77
Total hours (100hr per 10 credits) 200

Reading List

The reading list is available from the Library website

Last updated: 30/04/2025

Errors, omissions, failed links etc should be notified to the Catalogue Team