We aim to control anisotropy in lead-free perovskite analogues by pressure treatments for efficient sustainable solar cells. We will combine machine learning and in-silico screening with high throughput synthesis of lead-free perovskite analogues, to identify compounds exhibiting low anisotropy in their excited state and transport properties.
These compounds are then processed via hot pressing where their optoelectronic properties are studied by in situ electrical and optical spectroscopy.
Novel lead-free Perovskites
Supervisor: H. Oberhofer, Theoretical Physics, UBT
Co-supervisor: U. Bach, Engineering, MON
Thus PhD project focuses on the modelling, screening, design and synthesis of novel lead-free Perovskites or their analogues for use in back-contacted solar cell geometries.
In the Oberhofer group, the PhD student will perform first- principles calculations of charge transport properties in Perovskites and analogues.
These will serve as the basis of advanced machine learning models the student will employ to efficiently sample the Perovskite/analogue design space. Such generated data will be experimentally tested by the experimental partners in Bayreuth and Melbourne.
The ideal candidate should have a master's degree in physics or theoretical chemistry with a background in density functional and charge transport theory, possibly machine learning, and preferably some programming skills.