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Self-Driving Laboratories: Translating Materials Science from Laboratory to Factory.

Andre K Y Low1,2, Jayce J W Cheng2, Kedar Hippalgaonkar1,2

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Self-driving laboratories (SDLs) accelerate materials science by integrating manufacturing early. This approach reimagines the pipeline, collapsing timelines and improving success rates for faster innovation.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Automation

Background:

  • Traditional materials development faces a 10-20 year bottleneck from lab discovery to market.
  • Urgent technological challenges require faster innovation cycles.
  • Current automation in materials science is insufficient for rapid commercialization.

Purpose of the Study:

  • To propose self-driving laboratories (SDLs) as a fundamental reimagining of the materials development pipeline.
  • To advocate for integrating manufacturing constraints and scalability from the outset of discovery.
  • To present a roadmap for integrated materials development platforms.

Main Methods:

  • Critically examining current self-driving laboratory implementations.
  • Challenging prevailing assumptions about automation in materials science.
  • Proposing a shift from sequential to concurrent cross-scale development.

Main Results:

  • Self-driving laboratories can collapse the laboratory-to-factory timeline.
  • SDLs have the potential to improve reproducibility and success rates in materials development.
  • Concurrent cross-scale development is key to revolutionizing materials translation.

Conclusions:

  • Self-driving laboratories offer a paradigm shift beyond simple automation.
  • Integrating manufacturing and scalability early is crucial for efficient materials development.
  • A roadmap for integrated platforms can accelerate the translation of discoveries into commercial products.