Scalable multi-agent lab framework for lab optimization

Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises-how will they work together across large facilities? We explore one solution-a multi-agent laboratory-control framework. The framework is demonstrated with autonomous materials science labs in mind, where information from diverse research campaigns can be combined to address scientific questions. The framework can (1) account for realistic resource limits, e.g., equipment use; (2) allow for research-campaign-running machine-learning agents with diverse learning capabilities and goals; and (3) facilitate multi-agent collaborations and teams. The multi-agent autonomous facilities scalable framework (MULTITASK) makes possible facility wide simulations, including agent-instrument and agent-agent interactions. Through modularity, real-world facilities can come online in phases, with simulated instruments gradually replaced by real-world instruments. We hope that MULTITASK will open new areas of study in large-scale autonomous and semi-autonomous research campaigns and facilities.