Developing a scalable manufacturing technique for perovskite solar cells requires process optimization in high-dimensional parameter space. Herein, we present a machine learning (ML)-guided framework of sequential learning for manufacturing the process optimization of perovskite solar cells. We apply our methodology to the rapid spray plasma processing (RSPP) technique for open-air perovskite device fabrication. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best result from a device fabricated by RSPP. Our model is enabled by three innovations: flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint, incorporation of both subjective human observations and ML insights when selecting next experiments, and adaptive strategy of locating the region of interest using Bayesian optimization before conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods.

为钙钛矿太阳能电池开发可扩展的制造技术需要在高维参数空间中进行工艺优化。在此，我们提出了一种机器学习 (ML) 引导的顺序学习框架，用于制造钙钛矿太阳能电池的工艺优化。我们将我们的方法应用于用于露天钙钛矿器件制造的快速喷射等离子体处理 (RSPP) 技术。在筛选 100 个工艺条件的实验预算有限的情况下，我们证明了 RSPP 制造的设备的最佳结果是效率提高了 18.5%。我们的模型通过三项创新实现：通过将来自先前实验数据的数据合并为概率约束，在实验过程之间进行灵活的知识转移，在选择下一个实验时结合主观人类观察和机器学习洞察力，以及在对高效设备进行局部探索之前使用贝叶斯优化定位感兴趣区域的自适应策略。此外，在虚拟基准测试中，与传统的实验设计方法相比，我们的框架在有限的实验预算下实现了更快的改进。