用自动化+机器学习加速电致变色薄膜研发，提升涂层质量与效率。
A self-driving lab for solution-processed electrochromic thin films
- 构建自驱动实验室，融合自动采集与贝叶斯优化寻找最佳工艺参数。
- 实现无缺陷光滑涂层，显著提升电致变色器件在褪色与着色状态间的对比度。
- 适用于多种溶液加工材料，适合材料研发与工艺优化团队快速迭代。
溶液加工的电致变色材料在节能智能窗和显示领域具有巨大潜力，其性能受材料选择和制备条件影响。电致变色薄膜电极需表面光滑、无缺陷，以实现褪色与着色状态间最优对比度。旋涂法制备电致变色层的工艺复杂性制约了快速开发。本研究展示通过自驱动实验室加速电致变色涂层开发的可行性，结合自动化数据采集、图像处理、光谱分析与贝叶斯优化，高效探索工艺参数。该方法不仅提升通量，还能精准定位最优工艺条件。该策略可推广至多种溶液加工材料，凸显自驱动实验室在材料发现与工艺优化中的潜力。
原文摘要 · Abstract (English)
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.
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