95746 - Materials Modelling and Design

Learning outcomes

At the end of the course the students will learn advanced modeling and simulation techniques to predict materials behavior over different scales. They will understand how the fundamental physics necessary to describe materials in terms of electrons and atoms enters in numerical simulations and how it relates to the macroscopic function of materials. The electronic structure will be treated by quantum mechanical calculations, atomistic processes by molecular dynamics, mesoscale evolution by kinetic Monte Carlo and machine learning.Through hands-on sessions the students will become able to design and perform computer experiments using (and linking) the computational methods that are more suitable for the problem at hand. They will be challenged in integrating in silico experiments to real experiments for solving problems that are fundamental in nature and yet have great technological impact. Applied case studies include: Chemical reactions at surfaces and interfaces; Tribological (adhesion and friction), Mechanical and Electronic properties of solids and 2D materials.

Course contents

I. Methods for modeling:

1. Atomic Interactions

• Density Functional Theory in a nutshell
• Semi-empirical and Empirical Potentials
• Machine-learning based Force Fields

2. Chemical Reactions and Rare Events

• Transition State Theory
• Methods for finding the Minimum Energy Path on Potential Energy Surfaces

3. System evolution on the atomistic scale

• Introduction to Molecular Dynamics
• Ab initio Molecular Dynamics

4. Multiscale phenomena

 

• Kinetic Monte Carlo
  
• Machine learning based molecular dynamics

5. Materials Design

• High throughput, first-principles calculations

Readings/Bibliography

Books

• R. M. Dreizler and E. K. U. Gross Density functional theory: an approach to the quantum many-body problem
• D. S. Sholl and J. A. Steckel Density Functional Theory: A Practical Introduction
• D. Frankel and B. Smit Understanding Molecular Simulations
  
• E. B. Tadmor and R. E. Miller Modeling Materials

Reviews and research articles are also part of the course reference readings. The full bibliography will be provided within the lecture notes.

Teaching methods

Front lectures and practical sessions in the computational laboratory

Considering 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 places of study, [https://elearning-sicurezza.unibo .it/] in e-learning mode.

Assessment methods

The exam will be constituted of both a practical part focused on the execution of a computational research project and a colloquium.

Teaching tools

Blackboard, Slides, Computer applications

Office hours

See the website of Maria Clelia Righi