학술논문
What is missing in autonomous discovery: Open challenges for the community
Document Type
Working Paper
Author
Maffettone, Phillip M.; Friederich, Pascal; Baird, Sterling G.; Blaiszik, Ben; Brown, Keith A.; Campbell, Stuart I.; Cohen, Orion A.; Collins, Tantum; Davis, Rebecca L.; Foster, Ian T.; Haghmoradi, Navid; Hereld, Mark; Jung, Nicole; Kwon, Ha-Kyung; Pizzuto, Gabriella; Rintamaki, Jacob; Steinmann, Casper; Torresi, Luca; Sun, Shijing
Source
Subject
Language
Abstract
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows.