C9260 - MODELLING OF MATERIALS INTERFACES

Learning outcomes

Understanding of thermodynamic and kinetic processes in complex heterogeneous interfaces. Knowledge of main computational tools to model interfacial phenomena at the nanoscale. Ability to integrate modelling and experimental techniques to derive structure-property relationships.

Course contents

The course demonstrates, through specific applications cases, how the combination of fundamental theories and modelling approaches can assist in the design of new materials and the optimization of energy conversion and storage devices.

Topics covered:

• Thermodynamics and kinetics at surfaces and interfaces;
• Adsorption and diffusion;
• Interatomic potentials and surface forces;
• Monte Carlo methods;
• Molecular Dynamics simulations;
• Multiscale Modelling.

Readings/Bibliography

Hans-Jürgen Butt, Karlheinz Graf, Michael Kappl, “Physics and Chemistry of Interfaces”, Wiley

Ronald W. Shonkwiler, Franklin Mendivil “Explorations in Monte Carlo Methods”, Springer

Teaching methods

Class lectures and computational laboratory activities.

Attendance at the laboratory sessions requires all students to complete Modules 1 and 2 of the Safety Training for Study Environments in advance through the e-learning platform.

Note: Prior programming experience is helpful but not required. The basic concepts will be introduced during an introductory laboratory session, and the use of AI will be permitted as a programming support tool (i.e., as an expert sparring partner).

Assessment methods

Each student will be assigned a computational project. In general, a completed project should include a written report along with commented source code. The report should include (in a clear and concise way) any theory or analysis associated with the project, what the software is supposed to do, and how to use it. The report should include a discussion of validation runs as well as production
runs. Be sure to include results and conclusions.

The final oral examination will include a discussion of the project, an assessment of the theoretical knowledge acquired during the course, and a review of the laboratory activities.

While the use of AI is permitted to support the completion of the project (e.g., for problem solving and code development), the oral examination will assess the student's ability to critically analyze the methods, results, and conclusions presented in the project.

The assessment is graded on a 30-point scale (out of 30).

 

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities: it is recommended to contact the University office in charge (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ) in advance. The office will be responsible for proposing any accommodations to the interested students, which must in any case be submitted to the instructor for approval at least 15 days in advance. The instructor will evaluate their suitability in relation to the learning objectives of the course.

 

Teaching tools

The teaching material is made available to students in electronic format on https://virtuale.unibo.it/ .
Computational laboratories are equipped with Linux workstations and software for computational chemistry and physics.

AI will be used as a code assistant. 

Office hours

See the website of Francesco Segatta