Module manager: Dr Terry Kee
Email: T.P.Kee@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
A bachelor degree with a 2:1 (hons) in engineering, environmental science, physical science or mathematics discipline.
This module is not approved as an Elective
Students undertake an independent digital chemistry research project focused on the application of artificial intelligence, automation, and data science to chemical research, as appropriate to their programme and interests. Projects may involve computational studies, data-driven analysis, and/or digitally enabled experimental workflows. Students plan, implement, and report on their research outcomes with support from academic and technical staff, including guidance on the use of relevant software, automated systems, and data analysis techniques. Critical evaluation and interpretation of the data generated are supported by an academic supervisor for each project. However, successful completion of this module is highly dependent on the student’s engagement, independence, and initiative in driving progress and managing their research project.
1. To enable students to demonstrate their research capability and initiative in independently driving a digital chemistry research project involving artificial intelligence, automation, and/or data science;
2. To provide students with the opportunity to gain experience in planning, executing, and reporting a digitally enabled research project representative of those undertaken in industrial or academic chemical research environments;
3. To provide students with opportunities to present and communicate their research outcomes, including data-driven results, both orally and through professional scientific reports.
Subject specific learning outcomes:
1. Formulate research questions and hypotheses suitable for investigation using digital, computational, data-driven, and/or automated chemical research approaches;
2. Maintain accurate and formal records of research activities, including code, data, workflows, and metadata, using appropriate digital tools and platforms;
3. Design and refine computational studies, data analyses, or automated experimental workflows in response to earlier results;
4. Apply and critically evaluate key digital, computational, and analytical technologies relevant to chemical research, and/or develop new data-driven or automated methodologies;
5. Interpret complex experimental and computational data, and develop or validate models using statistical, machine-learning, or simulation-based approaches.
Skills learning outcomes:
a. Identify, evaluate, and synthesise relevant scientific literature, datasets, and digital resources addressing a significant problem in their chosen area of digital chemistry (Academic/Enterprise: Information searching, Academic: Referencing);
b. Effectively disseminate digital chemistry research outcomes, including data-driven and computational results, both orally and through appropriate written formats such as research papers, technical reports, or theses (Work-ready: Communication, Academic: Presentation skills, Academic writing, Academic language);
c. Plan, manage, and adapt research activities in a reflexive manner, responding to uncertain progress, evolving data, and the contributions of others, while demonstrating resilience and flexibility in the face of change or uncertainty (Work-ready/Academic: Time management, Academic: Reflection);
d. Make informed decisions based on own data, models, and incomplete or uncertain information, and critically analyse both their own results and the methodologies, results, and conclusions produced by others (Work-ready: Critical thinking, creativity, problem solving, analytical skills, decision-making; Academic: Critical thinking).
Project activities may include:
1. Critical review of relevant scientific literature, datasets, and digital resources to develop a clear understanding of the research topic;
2. Preparation of a clear, non-specialist description of the project aims and objectives, including consideration of ethical, legal, and responsible research and data-use aspects;
3. Development of an appropriate research methodology, including the selection and use of computational tools, software platforms, data science techniques, automated systems, and/or digitally enabled experimental design;
4. Planning and implementation of a research programme incorporating computational, data-driven, and/or automated experimental work;
5. Execution of computational studies, data analysis, and/or digitally enabled experimental workflows;
6. Analysis, interpretation, and critical evaluation of results, including model performance, data quality, and uncertainty;
7. Oral presentation of the research project and findings;
8. Preparation and submission of a final written research report.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 18 | 0.5 | 9 |
| Lecture | 2 | 1 | 2 |
| Practical | 48 | 7.5 | 360 |
| Seminar | 3 | 2 | 6 |
| Private study hours | 223 | ||
| Total Contact hours | 377 | ||
| Total hours (100hr per 10 credits) | 600 | ||
Students receive formative feedback through regular one-to-one supervisory meetings throughout the project, typically scheduled at agreed intervals (e.g., weekly), to review progress, discuss results, and address technical or methodological issues. Progress is monitored through discussion of ongoing work and interim outputs. In addition, students submit an initial research proposal, on which they receive written formative feedback to support refinement of the project aims, methodology, and plan before substantial project work is undertaken.
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Last updated: 29/05/2026
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