Engineering applications rely more and more on highly specialized materials exhibiting unique
functionalities. In recent years, for example, advanced functional materials such as hybrid
perovskites, metal-organic frameworks, and covalent organic frameworks have proven
invaluable to overcome many of the challenges associated with the development of highperformance photovoltaics, efficient heat management systems or stimulus-responsive sensor
materials. The rational design of such advanced functional materials requires insight at the
atomic level. In this respect, molecular modelling is an interdisciplinary field that allows gaining
information on the physical phenomena that govern the behaviour of these materials at the
nanoscale. It has attracted increasing interest due to the systematically growing computer
capabilities and the continuous optimization of physical models and numerical algorithms. The
application fields are very diverse, going from chemistry, molecular physics, solid-state physics,
and materials physics to nanophysics.
In this course, nanoscale modelling techniques are introduced by building upon concepts from
quantum mechanics, statistical physics, and atomic and molecular physics, focusing on the
applicability of these concepts and the rational approximations necessary to model real-life
nanostructured materials with industrial relevance. To model these nanosized functional
materials, a variety of simulation techniques are discussed and applied in this course. These
modelling techniques vary from quantum mechanics based methods, which are ideally suited to
study complex nanosystems of limited sizes or at restricted time scales, to classical force field
based methods, which are able to describe phenomena taking place on the microsecond scale
in systems of several tens of nanometers in size. These techniques are then applied to study
structural, mechanical, spectroscopic, and thermal properties of molecules and solids. The
course focuses on the development of functional materials for engineering applications in the
conversion and storage of energy, the sensing of chemical and physical stimuli, and heat
management on the nanoscale. The student will learn to work with different software packages
which are commonly used in scientific research.
The most common strategy to model nanoscale systems is to apply the Born-Oppenheimer
approximation, in which the electronic and nuclear degrees of freedom are decoupled. The
energy of the system then reduces to a parametric function of the position of the atomic nuclei.
The resulting multidimensional energy hypersurface is referred to as the potential energy
surface (PES) and governs the structural flexibility of the considered material. This course
demonstrates how the PES can be constructed from quantum mechanical information
(electronic-structure methods) or more approximate techniques (force fields), and how
adequate sampling of the PES allows recovering macroscopic properties of the material. These
methods are used to gain insight into materials behaviour at the nanoscale and develop design
strategies based on atomic information.
The course consists of the following main parts:
1 Introduction to molecular modelling: typical engineering applications, typical time and length
1 scales, interatomic interactions
2 Sampling techniques to derive macroscopic properties from the potential energy surface:
1 normal-mode analysis, partition functions, molecular dynamics, rare-event sampling
1 schemes, Monte Carlo approaches, vibrational spectroscopy
3 Many-body electronic-structure methods: Hartree-Fock, post-Hartree-Fock, density1 functional theory, electronic spectroscopy
4 Basis sets for the description of electronic states: localized basis sets, plane-wave basis
1 sets, pseudopotentials, projector-augmented wave method
5 Molecular mechanics to model larger systems on longer time scales: force field methods,
1 atom-in-molecule partitioning
6 First-principles materials design to rationally identify materials with outstanding performance
1 in, for instance thermal engineering (thermal conductivity, heat capacity), mechanical
1 engineering (elastic constants, structural flexibility), electronic engineering (band gap, charge
1 carrier mobility, UV/visible/infrared spectrum)
.