OVERVIEW

The course introduces some of the most important computational methods developed in the field of condensed matter physics, which are now widely applied in areas of applied physics such as materials science and biophysics. After an introduction aimed at placing these methodologies within the context of high-performance computing, the course provides an in-depth study of the Molecular Dynamics algorithm.

Several other computational techniques for exploring the Potential/Free Energy Landscape are also presented. These include global optimization algorithms, algorithms for locating minima and saddle points, and advanced sampling methods.

Through hands-on activities carried out during the course and in preparation for the exam, students will also have the opportunity to implement some of the proposed algorithms, perform simulations, and analyze the results.

AIMS AND CONTENT

LEARNING OUTCOMES

The Learning outcomes are:

- learning the concepts of energy landscape and free-energy landscape

- learning the principles underlying the methods for exploring the energy landscape for complex systems

- learning the concepts of collective variables and free-energy landscape

- learning the most important methods for exploring the energy landscape

- acquisition of the ability to elaborate and use software for the study of complex systems of interest in condensed matter physics and biophysics

AIMS AND LEARNING OUTCOMES

The course aims to introduce students to some of the most widely used methods in computational physics within the fields of condensed matter physics and materials science.

The expected learning outcomes are:

• Knowledge of Molecular Dynamics (MD), potential Energy Landscape theory and techniques for its characterization (global optimization, search for minima and saddle points), and advanced sampling techniques for the free energy landscape (Umbrella Sampling and Metadynamics methods).

• A detailed understanding of the basic MD algorithm, the ability to implement it, to verify its correct functioning, and to analyze simple outputs.

• The ability to independently develop a small project and present it clearly.
  Note: The project may consist, for example, of: extending the MD code developed during the course to simulate a specific system; implementing one of the global optimization techniques presented in class; applying, through the use of open-source software, one of the advanced sampling techniques covered in class; etc.
  More literature-based projects (for example, an in-depth study of a topic covered in class through the reading and discussion of scientific articles) are not excluded a priori, but must be discussed on a case-by-case basis with the instructor.

PREREQUISITES

Knowledge of basic statistical mechanics (statistical ensembles, partition function and its connection with free energy)

Programming in C++, Python, or (less recommended) MATLAB.

TEACHING METHODS

The course is delivered partly (approximately 36 out of 48 hours) through lectures, and for the remaining hours in a laboratory format.

• LECTURES: Classes will be conducted partly on the blackboard and partly using slides. The slides will be made available to students for independent study at home.

• LABORATORY: During the laboratory sessions, students will carry out programming activities under the supervision of the instructor. For this purpose, students may bring their own laptop, equipped with the necessary software to compile and run C++ or Python code (at the student’s choice), or use the equipment available in the DIFI computer lab.

SYLLABUS/CONTENT

In detail, the topics covered during the total 48 hours of the course will be:

• Introduction to computer simulations and high-performance computing – 1h

• Molecular Dynamics (MD): objectives and algorithms – 5h

• Implementation of a basic classical MD software – 14h (in-class, supervised)

• MD at different resolutions: the Car–Parrinello method, atomistic and coarse-grained force fields – 4h

• (Potential) Energy Landscapes – 3h

• Transition State Theory – 3h

• Global optimization techniques – 2h

• Saddle point search methods – 2h

• Free Energy Landscapes and advanced sampling techniques – 6h

• Applications – 4h

• Supervised work on the students’ final project – remaining hours

RECOMMENDED READING/BIBLIOGRAPHY

Lectures notes and slides

TEACHERS AND EXAM BOARD

Exam Board

GIULIA ROSSI (President)

RICCARDO FERRANDO

DAVIDE BOCHICCHIO (Substitute)

DIANA NELLI (Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of an oral examination, which includes the presentation of the individual project and answers to several questions on the course topics. The final grade will be the average of the grade assigned to the project (including the quality of the presentation) and the grade assigned to the oral assessment on the course contents.

For students with disabilities or specific learning disorders (SLD), please refer to the section “Further Information.”

ASSESSMENT METHODS

The student must be able to independently and critically develop the topic chosen for the individual project. This is assessed by evaluating the quality of the oral presentation and the slides, and by exploring the chosen topic further through relevant questions.

Knowledge of the fundamental concepts covered in the course is also assessed through additional questions not directly related to the individual project.

Exam schedule

FURTHER INFORMATION

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Sergio Di Domizio (sergio.didomizio@unige.it), the Department’s disability liaison.