OVERVIEW

The course introduces two fundamental computational methods for studying the properties of a wide range of materials (from synthetic to biological): Monte Carlo and molecular dynamics. After a theoretical introduction based on statistical physics, practical computer-based exercises will follow.

AIMS AND CONTENT

LEARNING OUTCOMES

By the end of the course, students will have acquired the fundamental theoretical knowledge and the necessary skills to model simple systems and simulate physical processes at the nanoscale.

AIMS AND LEARNING OUTCOMES

Knowledge and understanding

The student will acquire the fundamental theoretical knowledge required for the modelling and simulation of physical systems at the nanoscale. In particular, they will understand the statistical-physics foundations of Monte Carlo and molecular dynamics methods, including the Boltzmann distribution, the concept of ergodicity, and the principles of molecular dynamics in various statistical ensembles.

Applying knowledge and understanding

The student will be able to use Monte Carlo and molecular dynamics simulation codes to investigate the structural, thermodynamic, and kinetic properties of synthetic and biological materials. They will also be able to modify and adapt existing codes to different systems, including polymers, metallic nanoparticles, and lipid membranes.

Making judgements

The student will develop the ability to independently select the most appropriate simulation method to address specific physical problems, critically evaluating the results obtained and recognizing their limitations based on the model used.

Communication skills

The student will be able to clearly describe the methods used, the simulation results, and their physical significance, using appropriate scientific terminology.

Learning skills

The student will develop both conceptual and practical tools that will enable them to autonomously explore more advanced computational approaches in the future.

PREREQUISITES

No formal prerequisites are required. However, basic knowledge of mechanics, thermodynamics, statistics, and scientific programming is recommended.

TEACHING METHODS

Lectures are dedicated to the theoretical introduction of the computational methods covered in the course. Practical computer-based exercises allow students to apply the acquired concepts using simulation codes. The activities are designed to foster active learning and a strong connection between theory and practice.
Attendance is strongly recommended, especially for the computer-based practical sessions. Students will be guided in the use of simulation codes and in the progressive development of the skills required for the final project.

SYLLABUS/CONTENT

The course is divided into two parts, focusing respectively on Monte Carlo methods and molecular dynamics. Each part includes a theoretical introduction, aimed at providing the fundamental knowledge of statistical physics, followed by a practical section with computer-based exercises in which students will apply the acquired concepts using, and when necessary modifying, simulation codes.

Part 1: Monte Carlo

• Elements of probability and statistical mechanics: Boltzmann distribution

• Monte Carlo methods with importance sampling and kinetic Monte Carlo

• Practical simulations: application examples include magnetization in a two-dimensional ferromagnet, order-disorder transitions in lattice gases, and two-dimensional crystal growth.

Part 2: Molecular dynamics

• Fundamental principles of molecular dynamics

• Simulations at constant energy and at constant temperature

• Practical simulations: application examples include polymer systems, metallic nanoparticles (functionalized and non-functionalized), and lipid membranes.

RECOMMENDED READING/BIBLIOGRAPHY

Understanding Molecular Simulation: From Algorithms to Applications - Daan Frenkel, Berend Smit - ELSEVIER, 2nd Edition
Additional teaching material, slides, code examples and exercise instructions will be made available on AulaWeb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

According to the timetable reported here 

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral examination based on the presentation and discussion of a simulation project independently carried out by the student.
The work will consist of reproducing and extending one of the exercises carried out during the course, applied to a system agreed upon with the teacher. No intermediate tests are planned.

For students with disabilities or Specific Learning Disorders (SLD), please refer to the “Other information” section for specific arrangements.

ASSESSMENT METHODS

Learning will be assessed through an oral exam, during which the student will present a simulation project carried out independently. The student will be asked to replicate one of the exercises covered during the course, introducing modifications to the simulated system or material, in order to evaluate the ability to apply and adapt the computational methods taught.
The presentation will be followed by a theoretical question aimed at assessing the understanding of the statistical-physics principles underlying the methods used.
The final assessment will take into account methodological correctness, the quality of data analysis, the physical interpretation of the results, autonomy in developing the project, clarity of presentation, and the appropriate use of scientific terminology.

FURTHER INFORMATION

Students with disabilities and specific learning disorders (SLD): students with disabilities or specific learning disorders (SLD) may request accommodations for exams.

The relevant certification must be uploaded to the University website at servizionline.unige.it, in the “Students” section. The documentation will be checked by the University Office for Inclusion Services for Students with Disabilities and SLD (https://rubrica.unige.it/strutture/struttura/100111). Subsequently, well in advance of the exam date (at least 7 days before), students must complete the dedicated online form (https://modulionline.unige.it/richiesta-adattamenti#no-back).

For further information on how to request services and accommodations, please consult: https://unige.it/disabilita-dsa/richiesta-servizi.