Robotic data management for trustworthy AI: A perspective from the perovskite semiconductor community

Engineering notes

We use metal halide perovskites as a benchmark system, where high-throughput synthesis, rapid characterization, and closed-loop optimization are becoming mainstream research tools.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous laboratories are transforming materials research by combining robotic experimentation with artificial intelligence (AI) - guided discovery. As these platforms generate increasingly large and complex datasets, effective data management has become central to scientific progress. This consensus statement reflects the collective input of stakeholders from academia, industry, and government laboratories. Through interviews with principal investigators (PIs) of self-driving labs, it became clear that no perfect solutions currently exist. Most sections are headed by quotes from these discussions, reflecting the community consensus. We use metal halide perovskites as a benchmark system, where high-throughput synthesis, rapid characterization, and closed-loop optimization are becoming mainstream research tools. We review current robotic boxes and platforms, and examine how synthesis, fabrication, and characterization data are generated, curated, and managed across their lifecycle. We highlight key technical challenges, including fragmented data standardization, inconsistent metadata capture, cross-variable synchronization, multi-modal alignment and limited reproducibility. To address these challenges, we outline future directions that integrate large language models, AI agents, and personalized interfaces into laboratory workflows. Finally, we call upon the community to create incentives for timely, data-centric publishing and sharing to facilitate the construction of trustworthy AI models and push the boundaries of scientific discovery.

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