Machine Learning Perovskites
Aron Walsh a
a Faculty of Engineering, Department of Materials, Imperial College, London
Invited Speaker Session, Aron Walsh, presentation 016
Publication date: 6th February 2024

The translation of statistical techniques from the artificial intelligence community to materials science and engineering is helping to bridge the divide between traditional modelling and measurements [1]. In the study of metal halide perovskites, data-driven machine learning (ML) workflows are being used for diverse tasks ranging from novel materials discovery to closed-loop accelerated device optimisation. I will provide an introduction to this topic, with a focus on how the limits of materials modelling are being extended by incorporating ML techniques to develop deeper insights into the behaviour of perovskites across time and length scales [2,3]. In particular, I will discuss our latest understanding of compositional and structural disorder at the nanoscale, linked to multi-modal experimental characterisation [4]. The overarching goal is to shed light on the origins of the exceptional performance of these systems, as well as to identify routes to develop the next generation of perovskite-inspired materials. 

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