CME8129 : Modelling Materials and Processes

Semester 1 Credit Value: 20
ECTS Credits: 10.0


The aim of this module is to introduce the practical aspects of the basic methods used in the computational modelling of materials and processes. Both fundamental modelling and data-driven modelling techniques will be introduced.

Outline Of Syllabus

Time-scales and length-scales in materials structure and behaviour.
Interatomic potentials: cohesive energy, pair potentials, determining parameters in potentials.
Electronic structure methods: density functional theories, wave functions, pseudopotentials.
Molecular dynamics: atomic model in MD, embedded atom method, solutions for Newton’s equations of motion.
Multivariate statistical data analysis: linear regression, multiple linear regression, principal component analysis (PCA), principal component regression, ridge regression
Nonlinear regression: polynomial regression, orthogonal least square regression
Machine learning techniques: neural networks, radial basis function networks, extreme learning machines, recurrent neural networks, bootstrap aggregated neural networks, advanced topics in machine learning

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials61:006:00Non-synchronous online
Guided Independent StudyAssessment preparation and completion184:0084:00Assessment preparation and completion
Scheduled Learning And Teaching ActivitiesLecture121:0012:00PiP sessions
Scheduled Learning And Teaching ActivitiesSmall group teaching41:004:00Synchronous online
Guided Independent StudyIndependent study194:0094:00Review lecture notes and recommended texts as appropriate
Teaching Rationale And Relationship

Recorded lectures will be used to introduce the main topics. PiP lectures will be used to deliver material not covered in the recorded lectures and also to revise the content of recorded lectures.

The small group teaching online sessions are supervised activities in which the students apply the knowledge that they gain during lectures and private study to setup and run simulations and to analyse the results.

Plan B: In case of Covid-19 disruption:

• PiP lectures will switch to non-synchronous online learning (lecture material in Canvas)

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M50Problem Solving Exercise 1
Prob solv exercises1M50Problem Solving Exercise 2
Assessment Rationale And Relationship

The problem solving exercises provide an appropriate way to assess both theoretical understanding and practical problem solving skills and software skills. It also develops the ability to apply the broad base of scientific principles in conjunction with deeper knowledge and understanding in a specific subject area.

Reading Lists