Machine-Learning-Driven in-Device Optimization of All-Printed Perovskite Solar Cells

Metal halide perovskites offer a vast but largely unexplored compositional and processing space. High-throughput experimentation (HTE) integrated with machine learning (ML) is ideal for efficient exploration, preferably at the device level. However, multilayer deposition challenges often limit HTE to stand-alone materials. We address this by employing a screen-printed triple-mesoscopic architecture, offering stability and low-cost fabrication, enabling rapid in-device screening of up to 81 unique devices per batch. Our platform accelerates experimental throughput over 100× and reduces data variance to 25% of manual methods. We present a ML-driven workflow to identify optimal additive compositions within MAPbI₃, MAPbI₃/AVAI, and MAPbI₃/MACl compositional space that simultaneously enhance device efficiency and stability. Prior additive studies were performed individually in conventional contexts, whereas our HT/ML-assisted approach on full devices is unprecedented. Our approach achieves a 5.75-fold improvement over pristine MAPbI₃, validated across two experimental batches. Further analysis shows AVAI and MACl act synergistically─AVAI aids infiltration and early crystallization, while MACl suppresses long-term PbI₂formation─together enhancing carrier dynamics and stability.