The discovery and development of new molecules and materials remain constrained by slow, labor-intensive, and trial-and-error-driven experimental approaches, limiting progress in addressing challenges in renewable energy, sustainability, and specialty chemicals manufacturing. Conventional experimental workflows for semiconductor materials and catalytic systems rely on batch synthesis, which suffers from inconsistent reaction conditions and inefficient exploration of vast experimental design spaces. To overcome these limitations, we leverage advances in reaction miniaturization, robotics, automated experimentation, in-situ multi-modal characterization, and artificial intelligence (AI) to establish Data-Rich Autonomous Labs for accelerated materials and molecular discovery. By continuously evolving AI models and adapting experimental strategies under uncertainty, autonomous fluidic labs provide a scalable, data-driven solution for accelerating fundamental discoveries and industrial translation. In this talk, I will present recent advancements of our autonomous fluidic labs that integrate modular flow reactors, real-time feedback from high-throughput in-situ characterization, and AI-assisted decision-making to autonomously explore and optimize complex multi-step chemistries. These autonomous labs function as intelligent robotic co-pilots, capable of conducting more than 4,000 experiments per day, reducing materials discovery and development timelines from decades to mere weeks. Specifically, I will discuss the autonomous synthesis of colloidal quantum dots (metal halide perovskites and II–VI/III–V) for next-generation photonic and energy applications, demonstrating precise control over composition, size, and optoelectronic properties. Additionally, I will highlight the role of autonomous labs in catalyst discovery for specialty/fine chemicals, showcasing how an AI-driven experimental framework enables resource-efficient exploration of high-dimensional synthetic spaces (>10²⁰ possible conditions).
