Machine Learning Perovskites
Aron Walsh a
a Faculty of Engineering, Department of Materials, Imperial College, London
International Conference on Hybrid and Organic Photovoltaics
Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)
València, Spain, 2024 May 12th - 15th
Organizer: Bruno Ehrler
Invited Speaker Session, Aron Walsh, presentation 016
DOI: https://doi.org/10.29363/nanoge.hopv.2024.016
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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