Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior

  • Laurie J. Points
    WestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
  • James Ward Taylor
    WestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
  • Jonathan Grizou
    WestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
  • Kevin Donkers
    WestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom
  • Leroy Cronin
    WestCHEM, School of Chemistry, University of Glasgow, Glasgow G12 8QQ, United Kingdom

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<jats:title>Significance</jats:title> <jats:p>Exploring and understanding the emergence of complex behaviors is difficult even in “simple” chemical systems since the dynamics can rest on a knife edge between stability and instability. Herein, we study the complex dynamics of a simple protocell system, comprising four-component oil droplets in an aqueous environment using an automated platform equipped with artificial intelligence. The system autonomously selects and performs oil-in-water droplet experiments, and then records and classifies the behavior of the droplets using image recognition. The data acquired are then used to build predictive models of the system. Physical properties such as viscosity, surface tension, and density are related to behaviors, as well as to droplet behavioral niches, such as collective swarming.</jats:p>

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