66294 - Thermodynamics and Molecular Modelling

Course Unit Page

Academic Year 2019/2020

Learning outcomes

- fundaments of statistical thermodynamics and its applications to classical thermodynamics and spectroscopy
- molecular reaction dynamics and its applications to classical thermodynamics and spectroscopy
- fundaments of computational chemistry and its applications to classical thermodynamics and spectroscopy

Course contents

Prerequisites: a good knowledge of fundamental principles of mathematics, physics, quantum mechanics, thermodynamics, kinetics and molecular spectroscopy is required.

Program:
The main themes treated in this course are statistical thermodynamics and computational chemistry. The first theme will allow the student to connect the microscopic worl of quantum mechanics to the macroscopic world of thermodynamics and kinetics. The second theme will allow the student to learn how to quantum-chemically compute the microscopic and macroscopic properties studied in the first part of the course.

Contents of the theory part.

1) Statistical Themodynamics: fundamentals
- probability and statistics
- statistical ensembles and types of statistics

2) Equilibrium Statistical Themodynamics
- Statistical Themodynamics of ideal gas mixture
- thermodynamic properties

3) Non-Equilibrium Statistical Themodynamics
- chemical reaction kinetics
- chemical reaction dynamics

4) Thermodynamics and Spectroscopy: the computational approach
- computation: basic concepts
- computation: applications

Contents of the the exercise section.

5) Numerical exercises:

- probability and statistics
- calculation of partition functions (translational, rotational, vibrational, ...) and of the related properties
- calculation of thermodynamic and kinetic parameters

Contents of the computational lab part.

6) Computational lab practicals:

- quantum-chemical calculation of the spectroscopic properties required for evaluating the various types of partition function
- quantum-chemical calculation of thermodynamic properties

Readings/Bibliography

Lecture notes and projected slides play a fundamental role. These are available on the institutional-online repository for didactic material (IOL).

For further information, the followings text books are recommended:

1) P. Atkins - J. De Paula, Chimica Fisica, Zanichelli (IV edizione italiana)
2) D. A. McQuarrie - J. D. Simon, Chimica Fisica: un approccio molecolare, Zanichelli
3) D. A. McQuarrie, Statistical Mechanics, University Science Books (2000)

4) C. J. Cramer. Essentials of Computational Chemistry. Theories and Models. Wiley - 2nd edition
5) F. Jensen. Introduction to Computational Chemistry. Wiley - 2nd edition

Teaching methods

The course consts of three parts. The first part is a theory part and involves oral lectures supported by video-projection. The second part involves numerical exercises (carried out on the blackboard) aimed at applying the knowledge acquired in the theory part. Finally, the third part involved computational practicals aimed at applying the knowledge acquired in the first part. In detail, four exercitations will be carried out requiring four afternoons.

Assessment methods

Learning assessment is evaluated by means of the final (written) examination and reports on lab laboratory-practical reports (these should be submitted at least 2 days before the written exam). The written exam aims at verifying the student's knowledge and skills. The duration of this examination is on average 180 minutes and is organized as follows:

- Solution of about 15 short numerical exercises (similar to those solved during the course)

- Answer to about 25 questions (most of them: multiple choice) concerning the theoretical part.

During the written examination the use of the pocket calculator and text books are allowed (for the numerical exercise solution). The text book is required in order to consult the fundamental constants and conversion factors tables.

The final mark is the arithmetic mean of the marks obtained for: (1) numerical exercises, (2) answers to questions and (3) laboratory reports.

Teaching tools

1) Blackboard (lectures and exercises) and video-projector. Lecture notes
2) computational lab praticals

Office hours

See the website of Cristina Puzzarini