AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories

TL;DR

AlabOS is a flexible, Python-based framework designed to efficiently manage complex workflows and resources in autonomous laboratories, demonstrated through a prototype that synthesized thousands of samples over 1.5 years.

Contribution

It introduces a reconfigurable workflow model and resource reservation mechanism tailored for autonomous labs, enhancing experiment orchestration and conflict avoidance.

Findings

Successfully managed 3,500 samples over 1.5 years

Demonstrated effective workflow reconfiguration and resource allocation

Enabled conflict-free execution of diverse experimental tasks

Abstract

The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.