ABSTRACT Artificial intelligence (AI) has accelerated materials discovery, yet its translation to industrial manufacturing remains limited due to two critical gaps: the scarcity of proprietary industrial datasets and the absence of application‐oriented benchmarks. To address these challenges, we develop the AP‐Lab, an A I‐Driven Autonomous P ilot‐Scale Lab oratory workstation designed to bridge research and manufacturing. Using magnetic nanoparticles (MNPs) for viral nucleic acids (NAs) extraction as a case study, the AP‐Lab integrates four agent‐controlled systems for user interaction, optimization scheme generation, autonomous synthesis and testing, and data management. By leveraging localized industrial datasets and adopting Polymerase Chain Reaction (PCR) cycle threshold (Ct) values as an application‐specific benchmark, the AP‐Lab achieves rapid optimization of MNPs‐based NAs extraction products at pilot‐scale corresponding to 50,000 tests per batch within three weeks, and enables scale‐up manufacturing of 1 million tests per batch in two months. Compared to conventional manual workflows, the AP‐Lab reduces development timelines from four to six months to three weeks while delivering performance superior to leading commercial products. This work demonstrates a scalable strategy for AI‐driven pilot‐scale production and offers a blueprint for accelerating industrial adoption of advanced materials.
Wang et al. (Thu,) studied this question.