Chemistry

MARCO D'ABRAMO

At the end of the course, as regards the essential knowledge, the student must have acquired skills regarding the most suitable approaches to estimate kinetic and thermodynamic properties based on the complexity of the system.
The student is expected to have the ability to select the most suitable equations and formulas for solving quantitative problems and to know how to choose investigation methods suitable for studying the proposed systems (Dublin 1 and 2 descriptors).
Furthermore, the ability of analysis, synthesis, and logical coherence in the exposition and the student's ability to communicate in an appropriate language (Dublin 3 and 4 descriptors) will also be assessed through collective discussions in the classroom.
Finally, since it is a teaching of the Master's Degree in Chemistry, knowledge of the possible applications of investigation methodologies to solve chemical-physical problems will be appreciated.

The topics covered in this course concern the study and characterization of complex systems through computational methods.

By the end of the course, in terms of essential knowledge, the student should have acquired skills regarding the various theoretical/computational methodologies most commonly used for the study of condensed-phase systems. In particular, they should know the limits, approximations, and theories used to estimate the structural and physicochemical properties of such systems, the relevant quantities, and their physical significance at both the macroscopic and microscopic levels.

It is expected that the student will be able to contextualize the problem under examination, select the equations, formulas, and methods most suitable for solving quantitative problems. They should be able to choose the most appropriate methods for studying the proposed systems, demonstrating the application of the acquired skills. Additionally, they should be able to argue and defend their choices and conceive simple computational experiments based on the learned knowledge.

Furthermore, the student's ability to analyze, synthesize, and present logical coherence will be assessed, as well as their ability to communicate in an appropriate language.

Considering that the Molecular Dynamics course is part of the Master's Degree in Chemistry, for the study of the topics covered in the course, the use of multiple texts, some for in-depth analysis, and articles published in international journals, which will be discussed in class, is suggested. This approach should promote the ability to learn and the habit of selecting different bibliographic sources in both Italian and English.

The necessary prerequisites for a conscious learning of the topics covered in the course are classical thermodynamics (the three principles, chemical equilibrium, thermodynamics of solutions, phase transitions), statistical thermodynamics (principles, equilibrium statistical ensembles, microcanonical and canonical partition function, ideal gas model), quantum mechanics (principles, simple models such as harmonic oscillator, particle in a potential well, rigid rotor, atomic and molecular systems with a single electron, brief introduction to the solution of the Schrödinger equation for poly-electronic systems), classical spectroscopies (vibrational, electronic, fluorescence, nuclear magnetic resonance). Additionally, the student should be familiar with basic knowledge of mathematical analysis (derivatives and partial derivatives, differentials, integrals and differential equations, simple series) and physics (mechanics, electrostatics, and electromagnetism).

Review of statistical mechanics.

Classical Molecular Dynamics simulations: concepts, equations of motion, algorithms, and applications.

Simulations in different mechanical-statistical ensembles.

Monte Carlo methods.

Calculation of structural, kinetic, and thermodynamic properties. Convergence and reliability of calculated properties.

Calculation of free energy. Advanced sampling methods.

QM/MM hybrid methods: concepts, developments, and applications.

Introduction to programming and computer interaction: languages and terminal.

Advanced computation in scientific research.

Deep learning algorithms in chemistry and biophysics.

Frenkel and Smith, Understanding Molecular Simulations

The attendance mode is face-to-face/traditional.

The evaluation will be conducted through an oral examination in which the student will discuss the techniques used for the study of complex systems covered in the lectures (principles of the techniques and methodology of application). The ability to analyze, synthesize, and express oneself clearly will be assessed. Simple but significant systems will be discussed to evaluate the student's ability to contextualize them correctly and choose the appropriate study methodologies.

General questions about the topics presented in the lectures.