Master of Chemistry (MChem)

MChem Chemistry with International Study

Expand your Chemistry experience and opportunities with a year abroad.
  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: F104 / Institution code: M20
  • Key features:
  • Study abroad
  • Accredited course

Full entry requirementsHow to apply

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

Course unit fact file
Unit code CHEM40241
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
Assessment practical exam 2
Lectures 16
Practical classes & workshops 22
Independent study hours
Independent study 60

Teaching staff

Staff member Role
Cristina Trujillo del Valle Unit coordinator

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