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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Wu, Jianchang
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2024Inverse design workflow discovers hole-transport materials tailored for perovskite solar cellscitations
- 2024Inverse design workflow discovers hole-transport materials tailored for perovskite solar cellscitations
- 2024Self-driving AMADAP laboratory: Accelerating the discovery and optimization of emerging perovskite photovoltaicscitations
- 2024Unveiling the Role of BODIPY Dyes as Small‐Molecule Hole Transport Material in Inverted Planar Perovskite Solar Cellscitations
- 2023Enhancing Planar Inverted Perovskite Solar Cells with Innovative Dumbbell‐Shaped HTMs: A Study of Hexabenzocoronene and Pyrene‐BODIPY‐Triarylamine Derivativescitations
- 2023Integrated System Built for Small-Molecule Semiconductors via High-Throughput Approachescitations
- 2023Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cellscitations
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article
Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells
Abstract
<jats:title>Abstract</jats:title><jats:p>Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.</jats:p>