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ChemOS: An orchestration software to democratize autonomous discovery

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  • Loïc M Roch
  • Florian Häse
  • Christoph Kreisbeck
  • Teresa Tamayo-Mendoza
  • Lars P E Yunker
  • Jason E Hein
  • Alán Aspuru-Guzik

Abstract

The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.

Suggested Citation

  • Loïc M Roch & Florian Häse & Christoph Kreisbeck & Teresa Tamayo-Mendoza & Lars P E Yunker & Jason E Hein & Alán Aspuru-Guzik, 2020. "ChemOS: An orchestration software to democratize autonomous discovery," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0229862
    DOI: 10.1371/journal.pone.0229862
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    References listed on IDEAS

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    1. Dezhen Xue & Prasanna V. Balachandran & John Hogden & James Theiler & Deqing Xue & Turab Lookman, 2016. "Accelerated search for materials with targeted properties by adaptive design," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
    2. Vincenza Dragone & Victor Sans & Alon B. Henson & Jaroslaw M. Granda & Leroy Cronin, 2017. "An autonomous organic reaction search engine for chemical reactivity," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    3. Tim Chapman, 2003. "A structured approach," Nature, Nature, vol. 421(6923), pages 661-661, February.
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