Course content

A big part of modern materials research is based on computational models that can deepen our understanding of the fundamental mechanisms that drive different material and system properties as well as provide quantitative insight. This course will provide the theoretical foundation necessary for understanding several different computational modelling methods that are in use, at different length scales and using different strategies. The course will also provide practical examples of implementation and the students will need to implement these methods for simple model systems. The course will cover computational methods for model systems relevant to materials science, such as fluid dynamics simulations using finite element method, nano-scale molecules and materials using molecular dynamics and metropolis Monte Carlo, data mining to analyse complex experimental data, etc. The course will involve practical implementations of these methods in simple simulations.

Learning outcome

Knowledge:

• Mathematical foundation of basic numerical tools: (Runga Kutta, Finite Element Method, statistics)
• Have an overview of the different computational approaches that exist for modelling materials on different time and length scales and different amounts of detail.

Skills:

• Be able to write code that correctly runs simple models and interprets a computational model and represents a physical system
• Be able to correctly interpret simulation results and to draw correct conclusions and make predictions about the behavior of physical systems
• Be able to assess the applicability of a given model in order to both correctly choose the correct strategy and implementation for a given system and understand the limitations of interpretation.

Learning methods and activities

Lectures, programming assignments in groups, exercise classes.

The students will choose the set of computational methods they feel is relevant for their further studies. Each method will come with a set of digital learning tools and exercises, including code implementation. Each method will be accompanied with exercise hours where both the professor(s) and student assistants will be available for answering questions as well as helping with code implementation.

Further on evaluation

The course includes works counting 51% towards the grade and an oral presentation counting 49%.

Recommended previous knowledge

The student will benefit from, but is not required to have, knowledge in thermodynamics, statistical mechanics and fluid mechanics.

Required previous knowledge

• The student should be proficient in Python. TPK4186 or similar satisfies this requirement.
• The student should have a basic understanding of physics, chemistry and material technology. Courses in physics, chemistry and material technology at NTNU fulfill this requirement.
• The student needs a proper mathematical foundation in linear algebra and should understand and be able to use and solve equations represented using vectors and matrices. The course TMA4110 Matematikk 3 at NTNU or equivalent is sufficient.

Course materials

• To be specified