Kusne, A.
and McDannald, A.
(2023),
Scalable Multi-Agent Lab Framework for Lab Optimization, Matter, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935256
(Accessed November 21, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Scalable Multi-Agent Lab Framework for Lab Optimization
Published
Author(s)
,
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 – a multi-agent laboratory-control framework. The frame-work is demonstrated with autonomous material science labs in mind, where information from diverse research campaigns can be combined to address scientific ques-tions. The framework can: 1) account for realistic re-source limits, e.g., equipment use, 2) allow for research-campaign-running machine-learning agents with diverse learning capabilities and goals, 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 modu-larity, real-world facilities can come on-line in phases, with simulated instruments gradually replaced by real-world instruments. We hope MULTITASK opens new ar-eas of study in large-scale-autonomous and semi-autonomous research campaigns and facilities.Citation
Matter
Pub Type
Journals
Download Paper
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created April 11, 2023, Updated May 23, 2023