Module manager: Dr Benjamin Hanson
Email: B.S.Hanson@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
Basic Python programming skills (the ability to write loops, do maths and plot graphs) Basic mathematics skills (functions, calculus and summation) Basic knowledge of either: Statistical mechanics and thermodynamics Stochastic processes and Markov chains
PHYS3190 NATS3200
This module is approved as a discovery module
Since computers were invented, their ability to find numerical solutions to analytically unsolvable problems has been crucial in scientific progress. The most recent iteration of this is, of course, artificial intelligence (A.I.) in the form of neural networks and machine learning models (M.L.Ms.), which is becoming as ubiquitous in science as it is throughout the rest of society. In this module we will utilise both numerical simulation and artificial intelligence to analyse the thermodynamics and statistical mechanics of a variety of physical systems. From ideal gases to societal disease propagation, you will learn how computation methods such as the Monte Carlo method, Molecular Dynamics, and M.L.Ms., can be used to provide different levels of insight into highly complex problems. With a focus on practical application and exploration, you will begin to observe the overlap between these methods and where, and why, it is appropriate to use each one. Students will learn how to program and perform numerical simulations using the Python programming language and analyse the results to gain scientific insights. You will also learn the fundamentals of artificial intelligence; learning the mathematics of abstract fitting and regression on which it is founded and how to apply it to arbitrary datasets.
On completion of this module, students should be able to:
Understand the conceptual and theoretical frameworks of Monte Carlo and Molecular Dynamics simulations and their associated uncertainties.
Understand the conceptual and theoretical frameworks of Machine Learning and their associated uncertainties.
Determine where it is appropriate to use each type of model, and the epistemological assumptions made when doing so.
Realise the ubiquity of these methods throughout society.
On successful completion of the module students will be able to:
1) Perform Monte Carlo integration and Metropolis Monte Carlo simulations and analyse the results using appropriate scientific concepts
2) Perform Molecular Dynamics simulations and analyse the results using appropriate scientific concepts
3) Build regression and machine learning models and apply them to the analysis of physical datasets.
Skills Learning Outcomes
a) Program to a high level in Python.
b) Perform appropriate simulations and analysis of arbitrary numerical systems.
c) Manage time and continuously plan work to meet deadlines.
· Semester 1 Weeks 1-5: Introduction to Molecular Simulation and Molecular Dynamics
Topics Covered: Ideal Gases and Diffusion, Elastic Network Models and Principal Component Analysis, Lennard-Jones Gases and Structural Analysis
· Semester 1 Weeks 6-11: Monte Carlo Methods
Topics Covered: Numerical and Monte Carlo Integration, Metropolis Monte Carlo, The Ising Model, Generalised modelling and disease population models
· Semester 2 Weeks 1-6: Machine Learning Methods
Topics Covered: Data and Optimisation, Theoretical Modelling, Linear and Logistic Regression, Decision Trees, Neural Networks
· Semester 2 Weeks 7-11: Generalised and Combination Modelling
Topics Covered: Energy Barrier Hopping in Statistics, Dynamics and Machine Learning, Generalised Analysis of Molecular Dynamics Trajectories, Links between Machine Learning and the Scientific Method
Specific applications are subject to change, but overall topics will be as written here
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Practical | 11 | 2 | 22 |
| Private study hours | 156 | ||
| Total Contact hours | 44 | ||
| Total hours (100hr per 10 credits) | 200 | ||
156 hours of Private Study Time.
All workshops are attended by academic staff (and hopefully demonstrators) who engage with the students, give pointers and provide feedback where appropriate.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team