Scalable Multi-Agent Lab Framework for Lab Optimization

TL;DR

This paper introduces MULTITASK, a scalable multi-agent framework for autonomous laboratory management that enables resource-aware, collaborative, and phased integration of research agents and instruments in large scientific facilities.

Contribution

It presents a novel modular framework for multi-agent lab control that supports resource constraints, diverse learning agents, and phased real-world deployment.

Findings

Framework supports large-scale simulations of agent-instrument interactions.

Enables phased integration of real instruments into simulated environments.

Facilitates multi-agent collaboration across complex research campaigns.

Abstract

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 to this question - a multi-agent laboratory control frame-work. We demonstrate this framework with an autonomous material science lab in mind - where information from diverse research campaigns can be combined to ad-dress the scientific question at hand. This framework can 1) account for realistic resource limits such as equipment use, 2) allow for machine learning agents with diverse learning capabilities and goals capable of running re-search campaigns, and 3) facilitate multi-agent collaborations and teams. The framework is dubbed the MULTI-agent auTonomous fAcilities - a Scalable frameworK aka MULTITASK. MULTITASK makes possible facility-wide simulations, including agent-instrument and agent-agent interactions. Through MULTITASK's modularity, real-world facilities can come on-line in phases, with simulated instruments gradually replaced by real-world instruments. We hope MULTITASK opens new areas of study in large-scale autonomous and semi-autonomous research campaigns and facilities.