Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells36citations

Places of action

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Shi, Hongyang
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Wu, Jianchang
1 / 7 shared
Schmitt, Frederik
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Liu, Ziyi
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Brabec, Christoph J.
1 / 36 shared
Luo, Junsheng
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Wu, Zhenni
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Hauch, Jens A.
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Osterrieder, Tobias
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Zhang, Kaicheng
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Zhao, Yicheng
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Wagner, Jerrit
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Arnold, Simon
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Sutterfella, Carolin M.
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Liu, Bowen
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Stubhan, Tobias
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Heumueller, Thomas
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Sytnyk, Mykhailo
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Zhang, Jiyun
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Peters, Ian Marius
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2023

Co-Authors (by relevance)

  • Shi, Hongyang
  • Wu, Jianchang
  • Schmitt, Frederik
  • Liu, Ziyi
  • Brabec, Christoph J.
  • Luo, Junsheng
  • Wu, Zhenni
  • Hauch, Jens A.
  • Osterrieder, Tobias
  • Zhang, Kaicheng
  • Zhao, Yicheng
  • Wagner, Jerrit
  • Arnold, Simon
  • Sutterfella, Carolin M.
  • Liu, Bowen
  • Stubhan, Tobias
  • Heumueller, Thomas
  • Sytnyk, Mykhailo
  • Zhang, Jiyun
  • Peters, Ian Marius
OrganizationsLocationPeople

article

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

  • Berger, Christian G.
  • Shi, Hongyang
  • Wu, Jianchang
  • Schmitt, Frederik
  • Liu, Ziyi
  • Brabec, Christoph J.
  • Luo, Junsheng
  • Wu, Zhenni
  • Hauch, Jens A.
  • Osterrieder, Tobias
  • Zhang, Kaicheng
  • Zhao, Yicheng
  • Wagner, Jerrit
  • Arnold, Simon
  • Sutterfella, Carolin M.
  • Liu, Bowen
  • Stubhan, Tobias
  • Heumueller, Thomas
  • Sytnyk, Mykhailo
  • Zhang, Jiyun
  • Peters, Ian Marius
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>

Topics
  • perovskite
  • impedance spectroscopy
  • photoluminescence
  • experiment
  • thin film
  • power conversion efficiency
  • machine learning