TY - BOOK
T1 - Shaping the Future of Self-Driving Autonomous Laboratories Workshop
AU - Ferreira Da Silva, Rafael
AU - Moore II, Rob
AU - Mintz, Ben
AU - Advincula, Rigoberto
AU - Alnajjar, Anees
AU - Baldwin, Luke
AU - Bridges, Craig A.
AU - Coffee, Ryan
AU - Deelman, Ewa
AU - Engelmann, Christian
AU - Etz, Brian
AU - Firestone, Millie
AU - Foster, Ian
AU - Ganesh, Panchapakesan
AU - Hamilton, Leslie
AU - Huber, Dale
AU - Ivanov, Ilia N.
AU - Jha, Shantenu
AU - Li, Ying
AU - Liu, Yongtao
AU - Lofstead, Jay
AU - Mandal, Anirban
AU - Martin, Hector Garcia
AU - Mayer, Theresa
AU - McDonnell, Marshall
AU - Murugesan, Vijayakumar
AU - Nimer, Sal
AU - Rao, Nageswara
AU - Seifrid, Martin
AU - Taheri, Mitra
AU - Taufer, Michela
AU - Vogiatzis, Konstantinos
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.2172/2481197
DO - 10.2172/2481197
M3 - Commissioned report
BT - Shaping the Future of Self-Driving Autonomous Laboratories Workshop
CY - United States
ER -