About Autonomous Discovery

Revolutionizing scientific discovery

Autonomous discovery is transforming the way science is done, and Argonne National Laboratory is leading the charge. By integrating artificial intelligence (AI), machine learning and robotics, we are revolutionizing research to address some of the most profound challenges in energy, health and materials science. This initiative is not just about automating experiments — it’s about accelerating breakthroughs that will shape the future of science and society. 

Multipurpose labs for multidisciplinary research 

At Argonne, autonomous discovery begins with a bold vision: to create multipurpose, self-driving laboratories that empower our researchers across disciplines to solve complex problems faster and more efficiently than ever before. Our Robotic Autonomous Platforms for Innovative Discovery (RAPID) labs are designed to be adaptable, enabling scientists from diverse fields to use cutting-edge tools and technologies to advance their research. Whether it’s optimizing battery materials, advancing biotechnology or synthesizing materials for quantum applications, these labs are built to support a wide range of scientific endeavors.

Catalysts for innovation

The RAPID labs are more than just facilities — they are catalysts for innovation. Each lab is equipped with advanced robotics, AI-driven systems and high-throughput experimental platforms that work around the clock to identify patterns, test hypotheses and refine results. This approach allows researchers to focus their creative energies on interpreting data and making pivotal discoveries, while the labs handle the repetitive and time-intensive tasks. 

Impacting science and society

The impact of autonomous discovery is profound. By automating the research process, we are not only speeding up the pace of discovery but also elevating human creativity to new heights. Scientists can focus on solving the big-picture challenges — like developing advanced energy storage solutions and designing new materials for microelectronics — while autonomous systems handle the details. This approach fosters unbiased data collection, ensures rigorous evaluation and opens the door to groundbreaking innovations that would be impossible with traditional methods.