CH4P3-Advanced Techniques in Physical Chemistry

Module Provider: Chemistry
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Ricardo Grau-Crespo
Email: r.grau-crespo@reading.ac.uk

Type of module:

Summary module description:

Learn about modern physico-chemical measurement and modelling techniques from experts in their field. This module, taught entirely by external speakers from industry and national research facilities, will provide an accessible introduction into synchrotron science, machine learning and computational modelling, with minimal mathematical content (A level or equivalent) and assume no previous knowledge of programming. 

Aims:

To provide students with an understanding of modern physico-chemical measurement and modelling techniques.

Assessable learning outcomes:

Students should be able to describe the essential components, as well as the principles of operation of a selection of physico-chemical techniques, both experimental and computational. They should be able to use their understanding to draw quantitative conclusions about structures from data provided by the various measurement techniques. They should also be able to understand the physical basis of various computational approaches and assess the applicability of different modelling techniquesfor the theoretical investigation of different types of chemical problems of interest to the chemical industry.

Additional outcomes:

Students will gain hands-on, practical experience of the industrial application of computational methods for the study of chemical problems. Students will also see how X-ray and electron-based techniques are applied in research through a visit to the Diamond Light Source.

Outline content:

Characterisation of Materials with Electrons and X-rays (8 lectures, 2 tutorials and a visit to Diamond Light Source):



Physical principles (electronic structure of molecules and solids, excitation of electrons by photons, diffraction of electrons); Experimental requirements (creation of vacuum, X-ray sources, synchrotrons, electron energy analysers); Photoelectron spectroscopy; Auger electron spectroscopy; X-ray absorption spectroscopy (NEXAFS, EXAFS); Electron diffraction (LEED); Electron microscopy; Applications in Chemistry.

 

Machine Learning for Chemists (3h lectures, 3h computer-based practical classes):

Machine learning (ML) is becoming more important as data grows and industry and researchers look to leverage this data to discover new pharmaceuticals and materials. This course will provide an intro, with minimal mathematics or programming skills required, to the important tools that are used in modern cheminformatics environments including: Jupyter notebooks, scikit-learn, Keras and pandas.

Introduction to machine learning (types of ML; random forests, support vector machines, deep neural networks) Machine learning for chemistry (how to represent a chemical for a ML model; SMILES strings, graph networks, how to clean up data in preparation for machine learning). Application of machine learning (develop ML models to analyse IR spectra, develop ML model to predict physico-chemical properties of molecules for drug design).

Industrial Applications of Modelling (3h lectures, 3h computer-based practical classes):

Computational modelling is of growing importance in the chemical industry and has good employment prospects. This industrial-focussed course will provide a non-mathematical introduction to modelling techniques used for the in-silico design of consumer products like shampoo, laundry liquids, tea & deodorants. Specific topics include Design of Experiments, the Gaussian Process machine learning method, optimisation and pareto optimisation.

Brief description of teaching and learning methods:

Fourteen one-hour lectures and six hours of computer-based practical work, backed up with two tutorials and a visit to Diamond Light Source.

Contact hours:

	Autumn	Spring	Summer
Lectures		14
Practicals classes and workshops		8
External visits		3
Guided independent study:		75
Total hours by term	0		0
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Set exercise	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Two assessed tutorials on Characterisation of Materials with Electrons and X-rays = 50% (25% each)

Independent computer-based coursework = 50%

(Machine Learning – 25%; Industrial Applications of Modelling – 25%)

Formative assessment methods:

Students will receive formative feedback on their performance in the tutorials and computer-based practicals.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
An overall mark of 50%.

Reassessment arrangements:

Reassessment arrangements are in accordance with University policy. Failed coursework may be re-assessed by an alternative assignment before or during the August re-examination period.

Additional Costs (specified where applicable):

1) Required text books:  None

2) Specialist equipment or materials:  None

3) Specialist clothing, footwear or headgear:  None

4) Printing and binding:  None

5) Computers and devices with a particular specification:  None

6) Travel, accommodation and subsistence:  None

Last updated: 22 September 2022