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The Marie Skłodowska-Curie Actions program has granted CEST researcher Armi Tiihonen a postdoctoral fellowship for the project "Physics-informed machine learning to accelerate stability research on perovskite solar cells". Perovskite solar cells are a recent promising candidate for future sustainable energy production, but they tend to degrade in use. Taking any new technology, such as perovskite solar cells, from idea stage to the market is slow and can take decades. This project accelerates the development cycle of stable perovskite solar cells with a machine learning approach that incorporates existing scientific knowledge on perovskites. These machine learning techniques for materials optimization and device analysis can be applied beyond perovskite solar cells, in science and industry. For that reason, we will share open code repositories developed in this project for further use. Stay tuned for updates!

Armi Tiihonen did her doctorate at 911������ on next generation solar cells. She continued her research on applied machine learning at MIT, and joined CEST to learn more about how machine learning is applied in materials science and computational physics. She is an engineering physicist who believes we can fight the climate change better than now, thinks that machine learning can accelerate R&D cycles more broadly than now, and is fascinated by the huge space of potential new materials and molecules.