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Physics-informed machine learning to accelerate stability research on perovskite solar cells

CORDIS · observation · Publication date unknown

Physics-informed machine learning to accelerate stability research on perovskite solar cells The development of sustainable energy production technologies, such as new types of solar cells, has traditionally taken decades. These research cycles need to be accelerated in order to respond to the urgent climate change crisis. Perovskite solar cells (PSCs) are a recent technology that boast high electricity production efficiencies, however they suffer from insufficient lifetimes. Since there are thousands of material candidates even for a single layer of a PSC, it is challenging to search for stable materials and infer device degradation mechanisms. This project aims to implement machine learning (ML) as a part of the solution. I will create an accelerated, ML-assisted research cycle for improving the lifetime of PSCs: I will focusing on optimizing the composition of the light absorbing layer – perovskite –

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recordType

award

status

SIGNED

region

EU

value

199694.4

unit

EUR