Published January 2, 2025 | Version v2
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Shaping the Future of Self-Driving Autonomous Laboratories Workshop

Authors/Creators

  • 1. ROR icon Oak Ridge National Laboratory
  • 2. ROR icon United States Air Force Research Laboratory
  • 3. ROR icon SLAC National Accelerator Laboratory
  • 4. ROR icon University of Southern California
  • 5. ROR icon Argonne National Laboratory
  • 6. EDMO icon Johns Hopkins University, Applied Physics Laboratory
  • 7. ROR icon Sandia National Laboratories
  • 8. ROR icon Princeton Plasma Physics Laboratory
  • 9. EDMO icon University of Wisconsin-Madison
  • 10. ROR icon Renaissance Computing Institute
  • 11. ROR icon Lawrence Berkeley National Laboratory
  • 12. ROR icon Carnegie Mellon University
  • 13. ROR icon Pacific Northwest National Laboratory
  • 14. ROR icon North Carolina State University
  • 15. ROR icon Johns Hopkins University
  • 16. EDMO icon University of Tennessee Knoxville

Description

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

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