Undergraduate Course: Materials Simulation and Design (CHEE11035)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 5 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces advanced materials modelling methods, from molecular simulations to macroscopic continuum models and data-driven approaches, covering their strengths, limitations, and practical use cases with a hands-on approach. By the end of the course, the students will be able to orient themselves in the landscape of modelling methods and tools and be able to formulate an appropriate modelling strategy for a given materials design challenge. |
| Course description |
The course will consist of a mixture of lectures, case studies, and practical activities.
Introduction to materials modelling strategies:
macroscopic models, molecular simulations, data-driven approaches. Strengths, limitations, and typical use cases.
Molecular design and simulation:
Background of molecular methods for materials modelling: atomistic and coarse-grained simulations for the calculation of structural, thermodynamic, and mechanical properties.
Advanced thermodynamic models:
Statistical Equations of State such as Lattice models (Flory-Huggins theory, Sanchez Lacombe) and Perturbed Hard Chain Sphere methods (SAFT): introduction, theoretical foundations, how to implement in a Jupiter Notebook.
Data-driven methods for material selection:
Background on machine learning methods for materials selection: input features, model architectures, training procedure.
Multi-scale and multi method approaches:
Different strategies to combine modelling methods to overcome limitations: multi-scale simulations, hybrid physics-based/data-driven approaches.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 10,
Online Activities 10,
Formative Assessment Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam %: 100
Practical Exam %: 0
Coursework %: 0
|
| Feedback |
Quizzes and problems will be provided, alongside solutions, as self-study materials, to allow students to test their knowledge on selected topics. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe and compare advanced physics-based modelling tools available to predict materials properties relevant to engineering processes: applicability, limitations, interpretation of calculation results.
- Discuss the application of data-driven techniques applied to materials property prediction, demonstrating critical awareness of their advantages and limitations.
- Formulate an appropriate modelling strategy for a specific material, process, or application.
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Reading List
No specific textbook is required but the following resources can be consulted as supplementary reading material:
- Frenkel, Daan, and Berend Smit. Understanding molecular simulation: from algorithms to applications. Elsevier, 2023.
- Kontogeorgis, Georgios M., and Georgios K. Folas. Thermodynamic models for industrial applications: from classical and advanced mixing rules to association theories. John Wiley & Sons, 2009.
- Jelfs, Kim E., ed. Computer simulation of porous materials. Vol. 8. Royal Society of Chemistry, 2021.
- Butler, Keith T., Felipe Oviedo, and Pieremanuele Canepa. Machine learning in materials science. Vol. 29. American Chemical Society, 2022.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Materials design,Molecular simulation,Equations of state,Machine learning |
Contacts
| Course organiser | Dr Eleonora Ricci
Tel:
Email: ericci@ed.ac.uk |
Course secretary | Mr Mark Ewing
Tel:
Email: mewing2@ed.ac.uk |
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