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COMPUTATIONAL PHYSICS

CODE 101938
ACADEMIC YEAR 2021/2022
CREDITS
  • 6 cfu during the 2nd year of 9012 FISICA(LM-17) - GENOVA
  • 6 cfu during the 1st year of 9012 FISICA(LM-17) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR FIS/03
    LANGUAGE Italian
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 2° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course deals with the most important computational methods for the study of systems with many degrees of freedom in condensed matter physics. The initial part includes an introduction to molecular dynamics and to the basic principles of the Monte Carlo method. The course then continues with the discussion of methods for the study of the energy landscape, with applications ranging from the physics of nanoparticles, to biological systems and to the computer-aided drug design.

     

    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 student is expected to learn the fundamental elements for the characterization of the energy landscape and their role in determining equilibrium properties and transformation kinetics of complex systems.

    The student must be able to elaborate software for the simplest computational methods (molecular dynamics in unconstrained systems) and to use already prepared software for the study of more complex methods.

     

    PREREQUISITES

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

    Knowledge of at least one programming language (c ++, matlab, python ...)

     

    TEACHING METHODS

    Lectures and computer exrecises

    SYLLABUS/CONTENT

    The course is in collaboration with Dr. Giulia Rossi (Università di Genova) and Dr. Walter Rocchia (Italian Institute of Technology, IIT)

    The program is divided into the following parts
    -  Introduction to molecular dynamics (R. Ferrando)
    -  Energy Landscape (R. Ferrando)
    -  Structural optimization (R. Ferrando)
    -  Methods for accelerated exploration of the energy landscape (R. Ferrando)
    -  Free Energy Landscape and metadynamics (G. Rossi)
    -  Introduction to computer-aided drug design. Computational techniques for estimating chemical bond affinity (W.    Rocchia)

     

    RECOMMENDED READING/BIBLIOGRAPHY

    Lectures notes and slides

    TEACHERS AND EXAM BOARD

    Exam Board

    RICCARDO FERRANDO (President)

    DAVIDE BOCHICCHIO (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The exam is oral and consists of the presentation of a short seminar (20-25 minutes) on a topic chosen by th estudent, followed by a discussion. Finally, questions are asked about the parts of the course not directly related to the topic chosen for the seminar.

     

    ASSESSMENT METHODS

    The student is expected to be able to elaborate on the topic chosen for the seminar independently and critically. This is ascertained by evaluating the quality of the oral presentation and of the slides, and by asking relevant questions. The knowledge of the fundamental concepts explained in the course is ascertained with further questions not directly related to the topic of the seminar.