Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
Completion of level 2 of a Materials Science programme similar to that at Leeds, or equivalent.
| CHEM2372 | Fundamental Chemistry for Materials Science |
| CHEM3212 | Big Data, Big Science |
| CHEM3311 | Extended Topics in Inorganic and Materials Chemistry |
| CHEM3312 | Topics in Inorganic and Materials Chemistry |
CHEM3211, CHEM3291/92 (in part)
This module is not approved as a discovery module
To enable students to explore how to handle large datasets to extract key scientific, and apply this to different scientific questions. This will entail understanding how large datasets cab be useful and development of python programming skills. Furthermore, on completion of this module students will also be able to recognise, recall and explain structures, properties and bonding of a range of different classes of solid-state materials; and structure-property relationships in such materials and how these can be utilised in applications.
On completion of this module, students should be able to ...
Extract data from large datasets and learn the structures, properties and bonding for a range of different classes of solid-state materials.
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Extract key scientific information from large datasets
2. Describe and discuss confidently, accurately and in detail, using appropriate terminology, a range of topics in inorganic materials chemistry. Apply these to systematically solve complex problems across these areas. (C3)
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Extract key scientific information from large datasets
2. Describe and discuss confidently, accurately and in detail, using appropriate terminology, a range of topics in inorganic materials chemistry. Apply these to systematically solve complex problems across these areas. (C3)
Aggregating multisheet data using indirect functions
Fundamental python programming concepts
Pattern matching and data mining using python
Materials Structure and Bonding: crystal structure, nanostructure, defect structure, bonding types, electronic band structure.
Materials Properties: conductivity, mechanical properties, magnetism, properties of nanomaterials.
Materials Applications: energy storage, fuel cells, photovoltaics, magnets, biosensing, bioimaging
| 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
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Programming Project | 50 |
| Total percentage (Assessment Coursework) | 50 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) (S2) | 2.0 Hrs 0 Mins | 50 |
| Total percentage (Assessment Exams) | 50 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 12/05/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team