Chemistry

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.

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).

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.

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.

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).

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.