Supervisors: Dr. Bora Karasulu (Chem.), Dr. Albert Bartok-Partay (Phys.)

Summary:

Solar modules incorporating lead-halide perovskites now exceed the efficiency of conventional silicon modules. However, there is much still to be learned in order to drive further technology improvements. One area of interest is the presence of mobile ionic species, which have been shown to play a role in device efficiency, hysteresis and long-term stability.

The aim of this studentship sponsored by OxfordPV (https://www.oxfordpv.com/), a major player in photovoltaics technology, is to investigate the physical and chemical consequences of mobile ions diffusing around interfaces, for example those at grain boundaries and contact layers.

Project:

This project will make use of ab initio and machine learning techniques to provide in-depth insights that will help the continued development of state-of-the-art solar technologies. The PhD student will be studying the effect of perovskite stoichiometry, additives, defects and diffusion barrier layers on the interface topology and diffusion and diffusion mechanisms of absorber ions into the transport layers. PhD candidate will also develop novel machine-learned interatomic potentials (MLIPs) based on different models to enable longer and larger simulations of the solid-solid interfaces.

Materials explored:

• Various lead-halide perovskites of different chemical compositions
• Various contact layer materials typically used in perovskite solar cells
• Promising diffusion barrier materials

• Atomistic simulations using ab initio (DFT) and ML interatomic potentials (MLIPs)
• Possible connection to continuum-scale and device-scale modelling
• The presence of particles whose oxidation state changes during the simulations requires explicit treatment of electrostatics within the MLIP framework, calling for the extension of current ML models.

• Various surface characterisation techniques to corroborate simulation findings.