Lattice dynamics for materials modelling

Computational modelling is a widely-used tool in the Materials Sciences. The predictive power of modern electronic-structure techniques (e.g. density-functional theory) has taken theory from an explanatory role to being able to screen large numbers of materials for particular properties or even suggest systems that have not yet been discovered.

Standard modelling works from frozen snapshots of the atomic structure (e.g. from crystallography) and treats the atoms as being fixed to their equilibrium positions. This static model gives good results for many properties, but neglecting thermal motion is an approximation: the calculations have no concept of temperature, and many important properties depend on the structural dynamics. To bridge the gap between theory and experiments, fast, accurate and general approaches for modelling dynamics are needed.

The theory of lattice dynamics provides an infrastructure for modelling the lattice vibrations (phonons) in solids. These techniques can predict and assign vibrational spectra (e.g. IR, Raman),[1] assess the kinetic and thermodynamic stability of materials,[2] model how physical properties change with temperature,[3, 4] and study heat transport at the microscopic level.[5] The methods have broad applicability, but are rarely used.

The goal of this project is to establish the quantitative accuracy of lattice-dynamics modelling against experimental data. To start with, you will explore using the quasi-harmonic approximation to predict the temperature dependence of the structural, mechanical and electronic properties of binary materials (e.g. metal oxides and nitrides). This information will be used to set up a database of calculated thermal properties, which will be used to search for new structure-property relationships. Depending on your interests, you will apply the techniques you develop and benchmark to study more complicated systems, with the opportunity to engage in collaborative projects with other research groups in Chemistry, Physics and Materials Science.

Applicants are expected to hold, or be about to obtain, a minimum upper second class undergraduate degree (or equivalent) in Physics, Chemistry, Materials Science or other related discipline. A Masters degree in a relevant subject is desirable. Experience of computational materials modelling (e.g. using density-functional theory or force fields) is essential, and experience of working with periodic solids is particularly desirable. Experience of computer programming/scripting (e.g. Python, Matlab, etc.) and/or of working on Linux-based computing clusters is also desirable but not essential.

Informal enquiries are encouraged and should be addressed to [Email Address Removed].
https://skelton-group.github.io