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

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