B1721 - MODELLIZZAZIONE COMPUTAZIONALE

Learning outcomes

Al termine del corso lo studente avrà rivisitato i concetti di atomi, molecole, orbitali molecolari e la teoria del legame sulla base della teoria quantistica. L'insegnamento si propone di introdurre i metodi computazionali principali per i sistemi molecolari accanto alle principali tecniche di modellazione. Lo studente applicherà tali conoscenze in sessioni pratiche di laboratorio computazionale.

Course contents

The course is delivered in two modules with distinct contents but with joint computational laboratory activities. Module A illustrates the most common techniques for molecular modeling with quantum mechanical (QM) methods. Module B deals with the simulation techniques of complex systems with classical physics methodologies.

The main topics covered in the quantum simulation module are:

- Approximate resolution of the Schrödinger equation: atomic orbitals, basis functions and molecular orbitals, Hartree-Fock method applied to molecules

- Density functional method (DFT): theory and applications to the calculation of reaction paths

- Time-Dependent DFT method for the calculation of optical and excited state properties

- Description of the effect of matrices and solvents with implicit methods and hybrid QM/MM methods

The main topics covered in the classical simulation module are:

- Monte Carlo methods: theory, observables and applications

- Kinetic Monte Carlo: theory and applications to chemical kinetics

- Molecular Mechanics (MM) and force fields

- Molecular Dynamics simulations: theory, observables and applications

The computational laboratories, in addition to consolidating and showing practical applications of the topics covered during the lectures, will serve to illustrate concepts and software of molecular graphics and data analysis.

Readings/Bibliography

Christopher J. Cramer, “ESSENTIALS OF COMPUTATIONAL CHEMISTRY: theories and models”, Wiley, 2013.

Andrew R. Leach, “MOLECULAR MODELLING, principles and applications” Pearson education, 2001

Andrea Bottoni, “Introduzione alla Chimica Organica Computazionale”, EdiSES, 2023

Ronald W. Shonkwiler, Franklin Mendivil “Explorations in Monte Carlo Methods”, Springer, 2024

Daan Frenkel, Berend Smit “Understanding Molecular Simulation: From Algorithms to Applications”, Academic Press, 2023

Teaching methods

Frontal lessons supported by powerpoint presentations.

Several practical computational laboratory sessions and the preparation of reports for these activities are also planned.

In consideration of the type of activity and the teaching methods adopted, attendance of this training activity requires the prior participation of all students in modules 1 and 2 of training on safety in the workplace, [https://elearning-sicurezza.unibo.it/] in e-learning mode.

Assessment methods

The learning assessment will be carried out through: 1) the evaluation of the reports of the experimental and computer laboratories, based on the following criteria: organization, understanding of the experiment, clarity, completeness, readability and internal coherence. (50%) 2) an oral exam during which theoretical questions will be asked and the content of the reports will be discussed. (50%)

Teaching tools

Blackboard, video projector, computer lab, PowerPoint presentations.

All information and material about the course will be available on the platform virtuale.unibo.it

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Office hours

See the website of Luca Muccioli

See the website of