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April 10, 2026Digital Discovery0 citationsOpen Access

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs

LTLuca TorresiPFPascal Friederich

Key Points

  • The aim is to enhance decision-making processes in self-driving labs using a multi-stage Bayesian optimisation approach.
  • Developed a structured framework for dynamic pausing of experiments
  • Utilized intermediate measurements for efficiency
  • Implemented a multi-stage Bayesian optimisation model
  • Identified optimal solutions with higher efficiency than traditional methods
  • Showed improved resource allocation based on data-driven insights
  • Enabled more flexible experiment management through intermediate data evaluation

Abstract

A structure-aware, resumable framework enables dynamic pausing of multi-stage experiments via intermediate measurements. MSBO identifies optimal solutions more efficiently than traditional Bayesian optimisation via data-driven resource allocation.

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Cite This Study

Torresi et al. (2026) studied this question.

synapsesocial.com/papers/69d8948f6c1944d70ce057f4https://doi.org/10.1039/d5dd00572h
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