Designing perovskites with structural and compositional variation to maximise optoelectronic properties
Haoxin Mai a, Xuying Li a, Tu Le a, Dehong Chen a, Rachel Caruso a
a RMIT University, Melbourne, Australia
Invited Speaker Session, Rachel Caruso, presentation 057
Publication date: 24th October 2023

Optoelectronic and photocatalytic materials are major players for renewable energy generation, decreasing energy usage, and in environmental remediation. Perovskites have demonstrated potential as effective optoelectronic and photocatalytic materials. There are numerous possible elemental combinations that can be considered to discover effective perovskite materials, particularly when dopants are considered. We have been working on approaches to discover highly efficient, non-toxic perovskites for optoelectronic and photocatalytic applications.1,2 This includes materials for the photodegradation of pollutants, H2 production, photoluminescence, and photovoltaics. Given the tunable properties of perovskites and their range of structures, machine learning has been used to explore the vast perovskite materials space to guide the selection of materials for experimental synthesis. Additionally, the machine learning can be used to gain an understanding of underlying feature-property relationships. In this presentation, examples of these approaches will be given, showing variation in properties of the experimentally synthesised materials based on the dopant or the size and shape of the perovskites.

This work has been supported by the Australian Research Council through the Discovery Project Scheme (DP180103815 and DP220100945).

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