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Science acceleration and accessibility with self-driving labs.

Richard B Canty1, Jeffrey A Bennett1, Keith A Brown2

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Self-Driving Laboratories (SDLs) automate scientific research for efficiency. This perspective discusses improving SDL access through shared facilities and networks to boost collaboration and enable complex projects.

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Area of Science:

  • Chemical Sciences
  • Materials Science
  • Automation

Background:

  • Global challenges require innovative research methods.
  • Self-Driving Laboratories (SDLs) automate experimental design and execution.
  • SDLs optimize research processes and reduce material consumption.

Purpose of the Study:

  • Explore strategies for enhancing access to Self-Driving Laboratories (SDLs).
  • Address technical and collaborative challenges in SDL implementation.
  • Promote inclusivity and enable novel research through improved SDL accessibility.

Main Methods:

  • Perspective piece discussing centralized facilities and distributed networks for SDL access.
  • Analysis of technical requirements for SDL integration.
  • Examination of collaborative frameworks for human-machine and human-human interaction within SDLs.

Main Results:

  • Centralized facilities and distributed networks can improve SDL accessibility.
  • Overcoming technical and collaborative hurdles is key to realizing SDL potential.
  • Enhanced SDL access can foster inclusivity and facilitate complex research.

Conclusions:

  • Improving access to Self-Driving Laboratories is crucial for advancing scientific research.
  • SDLs have the potential to significantly enhance research efficiency and collaboration.
  • Facilitating broader access to SDLs will accelerate solutions to complex global challenges.