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Data-Driven Materials Manufacturing - Integrated Module
University College London (UCL)London, United Kingdom
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Program Details
- Degree
- Masters
- Major
- Chemical Engineering | Materials Engineering | Manufacturing Technology
- Area of study
- Engineering | Manufacturing and Processing
- Course Language
- English
Program Overview
Data-Driven Materials Manufacturing - Integrated Module (CENG0067)
Key Information
- Faculty: Faculty of Engineering Sciences
- Teaching department: Chemical Engineering
- Credit value: 30
- Restrictions: This module is only available to students enrolled on MSc Digital Manufacturing of Advanced Materials. Pre-requisites: CENG0068 Automation of Materials Manufacturing and CENG0069 High-Throughput Materials Chemistry.
Alternative Credit Options
There are no alternative credit options available for this module.
Description
Aim
To provide a practical platform that will allow students to integrate automated material synthesis, high-throughput data acquisition from material characterisation, and data analysis into a holistic form of materials engineering.
Synopsis
This lab-based module will provide students with a holistic learning platform to apply concepts and methodologies of accelerated optimisation in a practical context via data acquisition, analysis, and learning from large data sets. The content will be centered on solution-based materials, such as inorganic nanomaterials, liposomes, or proteins assemblies. Optimal nanoparticle design, liposome formulation, protein aggregation, colloidal motility, materials degradation, and self-healing are of crucial importance in tomorrow's solutions for vaccines, disease diagnostics, and drug delivery, but learned principles will be equally applicable to other industries. In each case, the project will contain routine use of automation elements, application of combinatorial synthetic procedures suitable for automation, materials characterisation via high-throughput screening, and the application of advanced statistical tool kits, including machine learning algorithms.
Learning Outcomes
- Apply theoretical concepts of high-throughput materials chemistry, fundamentals of data science, and automation of materials manufacturing hands-on in a laboratory environment.
- Apply principles of data science for problem-solving in experimental materials chemistry.
- Explain and exemplify how learning from big data sets can inform experiments in an iterative feedback loop following design – make – test cycles.
- Overcome technical challenges in materials fabrication through collaboration in a multidisciplinary team.
- Produce accurate and defensible oral and written reports in data-driven material optimisation.
Module Deliveries for 2026/27 Academic Year
Intended Teaching Term: Term 2
- Postgraduate (FHEQ Level 7)
Teaching and Assessment
Mode of Study
- In person
Intended Teaching Location
- UCL East
Methods of Assessment
- 80% Coursework (4 assessments)
- 20% Group activity
Mark Scheme
- Numeric Marks
Other Information
Number of Students on Module in Previous Year
- 8
Module Leader
- Dr Solomon Bawa
About University
UCL is a top-ranked university with life-changing research and a_x000D_
progressive approach to academia, making it different from the rest. At UCL,_x000D_
postgraduate students learn by doing—shaping the world around them with a_x000D_
hands-on approach.
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