The University of Southampton

CHEM2017 Quantitative Models in Chemistry

Module Overview

This module provides a practical introduction to the development and application of simple quantitative models in the chemical sciences. It adopts a context-based learning (CBL) approach in which the emphasis is on ‘learning by doing’. You will learn and practice the skills needed to develop quantitative models by working on three assessment briefs, each drawn from a ‘real-world’ situation - both in terms of the area of science (e.g. materials science, biodiversity management and forensic toxicology), and in terms of the context in which the model is required (e.g. providing expert witness report, preparing a technical analysis consultancy document). The module will help you discover how to apply quantitative (i.e. mathematical) reasoning to analyse or rationalise observations/data from the chemical and biochemical processes and more broadly from across the sciences. Developing models in the ‘real world’ will often involve you working with incomplete, partial or misleading information on the problem to be modelled, and a key aspect of this module is to help you develop your own strategies for working effectively in such situations such that by the end of the module you should have the confidence to tackle problems that are outside your area of technical knowledge. The CBL approach used in this module promotes and supports independent learning and enquiry, together with critical thinking. Key concepts and a general framework on how to approach quantitative modelling are outlined through short lecture-style presentations, but will not consider analytical mathematical modelling. The only knowledge of mathematics expected is that covered in the semester 1 Quantitative Methods Seminars that are part of CHEM1017. The bulk of the module entails substantial independent reading to learn the technical background of the problem being modelled. Supervised study sessions will allow you to discuss issues with the team delivering the module and to work with colleagues on the assessments. This module requires a high degree of self-motivation and good time-management skills as, unlike other year 1 Chemistry modules, it involves substantial unsupervised working and reading. In particular you should be aware that, because of considerable amount of reading around the assessment topic, you will need to balance this with the workload from other modules. This is a challenging module, in that the CBL approach will push most taking it out of their comfort zones, but one that will help you develop skills that you can apply in any area of science. [Note this module has replaced CHEM1023 and students who have been awarded credit for CHEM1023 may not register for this replacement module]

Aims and Objectives

Module Aims

(1) To develop an understanding of why quantitative models are needed in science and technology. (2) To develop an appreciation of some of the different types of models used to model natural phenomena. (3) To develop a critical understanding and how and when different types of models are used. (4) To develop basic ‘practical’ skills in model building and evaluation

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • understand the difference between qualitative and quantitative models
  • understand how quantitative models can be used to test hypotheses or infer mechanisms/processes
  • understand how models enable prediction/forecasting and allow estimates of reliability of predictions
  • set up simple quantitative models to infer quantitative relationships between observables and make predictions about the behaviour of the system studied
  • use simple quantitative models in ‘real world’ contexts to support decision making
  • use simple quantitative models in the critical evaluation of observations/data.


The module will comprise: (a) An overview of modelling: why? what? when? how? - discrete vs continuous models - numerical vs analytical vs algorithmic models - the qualitative to quantitative model spectrum (b) General methods used to develop models - use of formulæ as mathematical models - least squares - parameter adjustments to fit data - use of freeware kinetics modelling software (COPASI) (c) Modelling in Context; examples of ‘real world’ models drawn from three of the following: - Pharmacokinetics - Forensic toxicology - Materials science - Complex chemical/biochemical kinetics - Process modelling - Population dynamics - Geochemical processes - Oceanographic processes In addition to developing your model building and model evaluation skills, the module will enhance your independent-learning, problem-solving and critical-thinking skills as well as skills in locating, retrieving and processing scientific information and data sets.

Special Features

A special feature of this module is the need to have access to a laptop during the sessions. During the first session of the module you will be asked to form/join a self-selected study group that you will be part of for the rest of the module. A minimum of one laptop per study group will be required at all the sessions. It is recommended that to get the most from this module each student should bring their own laptop to the sessions

Learning and Teaching

Teaching and learning methods

Short Lecture style presentations will be used to present key knowledge, concepts and a general framework of modelling approaches Model development Guidance sessions will be used to guide students in the use of a simple mathematical toolkit for developing models. These sessions will exemplify the material covered during the lecture style presentations Supervised model development practice sessions will be used to enable students, through group work and supported by facilitators, to tackle problems of increasing complexity Unsupervised model development sessions will be timetabled to provide groups with a venue in which to work towards completing the assessments. Independent reading is an essential component of this module either because you need to learn the technical background of the problem that you are modelling, or because you need to find a value for a parameter in your model. This will be challenging as it will involve reading review type papers as well as primary research papers.

Preparation for scheduled sessions24
Completion of assessment task74
Total study time150

Resources & Reading list

Matt A. Bernstein, William A. Friedman. Thinking About Equations: A Practical Guide for Developing Mathematical Intuition in the Physical Sciences and Engineering. 

P. Monk, L.J. Munro (2010). Maths for Chemists. 

E. Steiner (2008). The Chemistry Maths Book. 

An introduction to mathematical modelling.

Overview of models in scientific decision making.

Mathematical modelling (teacher package.

Open University Course Team. Introduction to Mathematical Modelling. 

John Berry, Ken Houston. Mathematical Modelling (Modular Mathematics Series). 


Assessment Strategy

The module will entail three assessments. They will be in the form of “briefs’ that set context of the problem to be modelled. Each assessment will have explicit assessment criteria against which it will be marked. A small number of resources will be made available on Blackboard for each assessment to enable you to get started, but you will be expected to find any additional relevant information (which can be substantial) for yourself. The final assessment will be more substantive than the first two, and during the final session of the module, a number of students will be selected to give a 5 minute oral presentation, on the way they answered the assessment brief, to the rest of the class.


MethodPercentage contribution
Assessment 45%
Assessment 55%


MethodPercentage contribution
Assessment 100%
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