Autonomous chemical laboratory

Reimagining Materials Discovery Through Autonomous Laboratories

The discovery of new materials remains a complex and time-consuming process, often taking 10 to 30 years from initial discovery to commercial application. To accelerate this timeline, the development and adoption of autonomous laboratories has become increasingly urgent. As we move deeper into the Fourth Industrial Revolution, the traditional concept of a laboratory is rapidly evolving. Repetitive and labor-intensive experiments, once the domain of graduate students and researchers, are being replaced by intelligent systems. The human role in laboratories is diminishing as AI and robotics take on a greater share of experimental work—executing tasks more efficiently and intelligently.

In response to this shift, the CAIM Lab is committed to spearheading the development of autonomous laboratories for materials research. Building such labs is inherently multidisciplinary, requiring the integration of several distinct research domains. Artificial intelligence plays a crucial role in predicting material properties and recommending synthesis pathways. Meanwhile, robotics and computer vision are essential for automating experimental procedures and interpreting results. The CAIM Lab aims to merge these diverse technologies into a cohesive and functional platform. Over the next decade, the CAIM Lab will intensify its efforts to realize fully autonomous laboratories and promote their broader adoption, contributing significantly to the transformation of materials science.

    • Bespoke Metal Nanoparticle Synthesis at Room Temperature and Discovery of Chemical Knowledge on Nanoparticle Growth Via Autonomous Experimentations

      Advanced Functional Materials 34, 2312561 (2024)

    • OCTOPUS: Operation Control System for Task Optimization and Job Parallelization via a User-Optimal Scheduler

      Nature Communications 15:9669 (2024)

    • Machine Vision-based Detections of Transparent Chemical Vessels Toward the Safe Automations of Material Synthesis

      npj Computational Materials 10, 42 (2024)

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Material-generative AI