A self-driving lab for solution-processed electrochromic thin films
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909946673627136 |
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| author | Dahms, Selma Torresi, Luca Bandesha, Shahbaz Tareq Hansmann, Jan Röhm, Holger Colsmann, Alexander Schott, Marco Friederich, Pascal |
| author_facet | Dahms, Selma Torresi, Luca Bandesha, Shahbaz Tareq Hansmann, Jan Röhm, Holger Colsmann, Alexander Schott, Marco Friederich, Pascal |
| contents | Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A self-driving lab for solution-processed electrochromic thin films Dahms, Selma Torresi, Luca Bandesha, Shahbaz Tareq Hansmann, Jan Röhm, Holger Colsmann, Alexander Schott, Marco Friederich, Pascal Machine Learning Materials Science Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization. |
| title | A self-driving lab for solution-processed electrochromic thin films |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2512.05989 |