Course Description


The course offers an introduction and thorough deepening in computational methods in Materials Physics and Technology. The course will analyze the available computational methods at the atomic scale, at the mesoscopic level as well as at the macroscopic scale. We will first analyze the calculations of first principles-ab initio (Hartree (H), Hartree Fock (HF), Density functional theory (DFT), Linear Augmented Plane Wav (LAPW), Linear combination of atomic orbitals (LCAO) ), as well as the Tight Binding (TB) calculations which is the most simplified form of atomistic interaction taking into account the electronic structure of the matter. Afterwards, Molecular Dynamics and Monte Carlo calculations will be analyzed, either using first principles but mainly based on interatomic potentials. Then we will give the basic principles of the continuum theory of matter for macroscopic scale simulations. The basic theoretical principles and algorithms of the above methods will be analyzed as well as the limitations of their applications. In addition, computational methods of data analysis, Artificial Intelligence, Machine Learning and Deep Learning will be analyzed. Data analysis methods are a key component of experimental data processing and data mining from large scale calculations.

Furthermore, will be analyzed applications of computational methods on modern computational problems in Physics of Materials such as computational modeling and analysis of crystal structures, periodic boundary conditions, creation of surfaces, interfaces and extended defects. Calculations of lattice constants by the use of interatomic potentials. Energetic calculations – methods of energy minimization. Analysis of structural and electronic properties by the use of first principles calculations on crystalline materials. Band gap of semiconductors, density of states, statistical thermodynamics and properties of matter.

 

 

Course Info


Code:  ΠΥΥ102

Group:  Compulsory

Semester:  First Semester

Hours / Week:   3

ECTS Units:  5

Instructors:  Ι. Kioseoglou