Structural Analysis of Cobalt Ferrite using DFTU+V

The Chair of Neuroengineering Materials at TUM CIT is seeking a highly motivated master’s student to contribute to understanding nanoparticles as nanoelectrodes in the cutting-edge field of neuroengineering.

At Kozielski Lab, we are actively investigating magnetoelectric nanoparticles (MENPs) as a breakthrough solution for a truly wireless method to stimulate the nervous system, providing a non-invasive and more effective solution.

The combination of magnetostrictive (cobalt ferrite) and piezoelectric phases (barium titanate) allows conversion of an externally applied magnetic field to electric stimulation (magnetoelectricity), but on a nanoscale leads to unique behaviour of MENPs which we hope to understand by probing the crystal structure of the individual phases. Therefore, to understand and predict the overall behaviour of these nanoparticles, the goal of this thesis will be to calculate the mechanical and magnetic properties of the magnetostrictive phase-cobalt ferrite.

In cobalt ferrite, cobalt and iron occupy octahedral and tetrahedral positions in a perovskite structure to form a normal spinel structure. Substitution of cobalt into the sites occupied by iron leads to formation of increasing partially inverse structure which exhibit different magnetic properties. Existing computational methods will be applied to develop a bottom-up model where the candidate needs to calculate the mechanical properties as well as total magnetic moment (Bohr magneton) of Co_xFe_3-xO₄ (x = 1, 1.5, 2). This will be done via DFTU+V implementation in Quantum Espresso and compared with literature and experimental values.

Requirements:

Knowledge of computational quantum mechanical modelling
Basic know-how of running electronic-structure calculations using open-source codes (preferably Quantum Espresso)
Familiarity with crystal visualization software such as VESTA
Interest in quantum modelling of magnetic materials

Advantageous:

Ability to create High Performance Computing (HPC) workflows
Set up Virtual Machines
Knowledge of Python

If you are interested in this position, kindly send your detailed CV and current transcript to p.kumari@tum.de.