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.
@article{arxiv.2405.13930,
title = {AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories},
author = {Yuxing Fei and Bernardus Rendy and Rishi Kumar and Olympia Dartsi and Hrushikesh P. Sahasrabuddhe and Matthew J. McDermott and Zheren Wang and Nathan J. Szymanski and Lauren N. Walters and David Milsted and Yan Zeng and Anubhav Jain and Gerbrand Ceder},
journal= {arXiv preprint arXiv:2405.13930},
year = {2024}
}