A self-driving lab for solution-processed electrochromic thin films
This work addresses the slow development of solution-processed electrochromic materials, which is incremental as it applies existing self-driving lab methods to a specific domain.
The study tackled the challenge of optimizing spin-coated electrochromic thin films for smart windows and displays by using a self-driving lab that combines automation and machine learning, resulting in increased throughput and efficient parameter exploration.
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.