- UCAS course code
- F104
- UCAS institution code
- M20
Master of Chemistry (MChem)
MChem Chemistry with International Study
- Typical A-level offer: A*AA including specific subjects
- Typical contextual A-level offer: AAA including specific subjects
- Refugee/care-experienced offer: AAB including specific subjects
- Typical International Baccalaureate offer: 37 points overall with 7,6,6 at HL, including specific requirements
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £36,000 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances. For information about scholarships/bursaries/sponsorship please see our undergraduate fees pages and visit our School website .
Course unit details:
Computational Modelling Techniques
Unit code | CHEM40241 |
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Credit rating | 10 |
Unit level | Level 4 |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | No |
Overview
The revised CHEM40241 unit will provide both theoretical and practical foundations of modern computational chemistry over 12 weeks. Students will learn about various computational methods and their applications across a broad range of chemical topics, from molecular orbital theory to machine learning for materials and molecular properties. The course will emphasise the integration of computational chemistry in understanding and predicting molecular phenomena.
Pre/co-requisites
The course is accessible to students with no previous computer programming experience. Elementary concepts of programming will be introduced. The application of the ideas will be illustrated with examples drawn from a range of chemical problems.
Aims
The course aims to introduce fundamental principles of scientific computational modelling in the chemical sciences. A range of modelling approaches, numerical algorithms, and the core concepts of computational chemistry will be covered. Students will also develop practical skills through the study of examples using advanced computational tools, including software such as ORCA, VASP, and machine learning frameworks.
Learning outcomes
On successful completion of the course, students should be able to:
- Demonstrate proficiency in basic Linux commands and the ORCA software environment.
- Apply various computational methods to chemical problems, including DFT, WFT, and solid-state theory.
- Perform calculations on molecular structures and interpret computational data. ·
- Explore material properties, reaction mechanisms, and spectroscopic behaviours using computational methods. ·
- Integrate machine learning tools in the computational chemistry context.
Syllabus
Basics
• Introduction to basic Linux commands.
• Introduction to ORCA software: Preparing and submitting calculation input files, analysing output.
Basis sets
• Introduction to atomic orbitals and basis sets.
• Creating geometry optimisation input files with various basis sets.
• Selection of appropriate basis sets for different applications.
- Lectures
- Practical workshops - scripted examples and problems using computer package (such as Matlab)
- Online support using Blackboard (self-study and self-assessment materials)
Transferable skills and personal qualities
Ability to work with computational chemistry software, including ORCA, VASP, and machine learning frameworks.
Strong problem-solving skills applied to chemical data and molecular phenomena.
Competence in interpreting computational results in the context of real-world chemical systems.
Familiarity with solid-state physics, molecular modelling, and electronic structure theory.
Assessment methods
- Practical skills assessment, online examination (open book final assessment) 2hr duration. Weighting within unit is 70%.
- x3 quizzes during workshop. 15 minutes for each quiz. Weighting within unit is 30%.
Feedback methods
Online support materials include workshop and self study exercises (formative assessments) that allow students to engage in problem solving activities.
General assistance and feedback from staff during weekly practical workshop sessions.
Personal feedback on lecture material and workshop examples throughout the course.
Recommended reading
Best-Practice DFT Protocols for Basic Molecular ComputationalChemistry Angew. Chem. Int. Ed. 2022, 61, e20220573
Quantum chemistry: Molecular structure and properties in Silico (RSC Theoretical and Computational Chemistry Series), J. J. W. McDouall, RSC Publishing, 2013.
Solids and Surfaces: A Chemist's View of Bonding in Extended Structures
Roald Hoffmann
ISBN: 978-0-471-18710-3
Essentials of Computational Chemistry: Theories and Models, 2nd Edition
Christopher J. Cramer
ISBN: 978-0-470-09182-1
Study hours
Scheduled activity hours | |
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Assessment practical exam | 2 |
Lectures | 16 |
Practical classes & workshops | 22 |
Independent study hours | |
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Independent study | 60 |
Teaching staff
Staff member | Role |
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Cristina Trujillo del Valle | Unit coordinator |