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MChem Chemistry with Medicinal Chemistry / Course details

Year of entry: 2021

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Course unit details:
Computational Modelling Techniques

Unit code CHEM40241
Credit rating 10
Unit level Level 4
Teaching period(s) Semester 1
Offered by Department of Chemistry
Available as a free choice unit? No


Weeks 1–3: Introduction to data structures, programming and algorithms (Dr N A Burton, 3 lectures + 3 workshops)

Weeks 4–6: Linear algebra techniques in molecular structure (Dr J J W McDouall, 3 lectures + 3 workshops)

Weeks 7–9: Numerical techniques for chemical data analysis, molecular modelling, kinetics and dynamics (Dr N A Burton, 3 lectures + 3 workshops)

Weeks 10–12: Numerical techniques in electronic structure theory  (Dr J J W McDouall, 3 lectures + 3 workshops)



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.


The course aims to introduce some principles of scientific computational modelling in the chemical sciences. A range of modelling approaches, numerical algorithms and the generic concepts of computer programming will be introduced. Students will also develop practical modelling skills through the study of a range of examples using the common computational framework of MATLAB.

Learning outcomes

On successful completion of the course students should be able to:

  • Knowledge of MATLAB syntax and the ability to write MATLAB scripts and functions.
  • Understand the generic concepts involved in building an algorithm to solve a problem numerically.
  • Familiarity with a few widely used numerical techniques of linear algebra and ODEs.
  • Proficiency in decomposing (unseen) mathematical problems into numerical solutions.

Teaching and learning methods

  • 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

• Knowledge of MATLAB syntax.

• Ability to write MATLAB scripts and functions.

• Understand the generic concepts involved in building algorithms.

• Familiarity with widely used numerical techniques.

• Ability to decompose unseen mathematical problems into numerical solutions.


Assessment methods

Method Weight
Written exam 100%

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

A Guide to MATLAB:  For Beginners and Experienced Users, B.R Hunt; R.L. Lipsman, J.M. Rosenberg, K.R. Coombes, J.E. Osborn, G.J. Stuck; Cambridge University Press, 2006, online 1012;

Matlab: A practical introduction to programming and problem solving, S. Attaway, Butterworth-Heinemann, 3rd Edition, 2013

Introduction to algorithms, T. H. Cormen, C. Leiserson, R. Rivest, C. Stein, MIT Press, 3rd Edition, 2009

Computational quantum chemistry: Molecular structure and properties in Silico (RSC Theoretical and Computational Chemistry Series), J. J. W. McDouall, RSC Publishing, 2013.


Study hours

Scheduled activity hours
Assessment practical exam 2
Lectures 12
Practical classes & workshops 24
Independent study hours
Independent study 62

Teaching staff

Staff member Role
Joseph McDouall Unit coordinator

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