Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Use advanced modelling software to investigate complex chemical problems
- Write and catalogue new code to a professional standard
- Use computational approaches to test and develop models that answer chemical questions
Syllabus
Computational skills to be developed
1. Setting up operating systems on virtual machines
2. Development of new code through the use of Jupyter notebooks with appropriate version control.
3. Development of models based on your chemical knowledge and write code to test these against literature results.
4. You will learn about the nuts and bolts of running large scale computers covering the generation and population of repositories, schedulers and queueing systems and libraries.
5. You will be introduced to fundamental ideas behind AI and ML through optimsation and least squares fitting strategies.
6. You will perform and analyse molecular dynamics simulations
Personal, written and presentation skills
1. You will learn how to use the Jupyter notebooks to both code and report on your findings.
2. You will learn how to present and catalogue complex data.
Learning and Teaching
Teaching and learning methods
Practical sessions, supported by pre-labs and some written assignments.
| Type | Hours |
|---|---|
| Preparation for scheduled sessions | 25 |
| Specialist Laboratory | 85 |
| Independent Study | 20 |
| Completion of assessment task | 20 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| A lab report | 20% |
| Laboratory work and associated tasks | 60% |
| Student presentation | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|