Artificial intelligence is transforming chemical research, yet the scarcity of high-quality, standardized experimental data limits its potential. Microfluidic technologies offer a powerful solution by enabling data-dense, high-throughput reaction generation and real-time analytical integration. These platforms reduce reagent consumption, enhance reproducibility, and provide rich, machine-readable datasets essential for training reliable AI models. By linking rapid reaction execution with advanced analytics, microfluidics bridges critical gaps in current autonomous and data-driven workflows. This Perspective highlights recent advances, identifies key bottlenecks, and outlines how microfluidics can serve as the foundational infrastructure for future AI-enabled, fully autonomous chemical discovery.
Warias et al. (Wed,) studied this question.