Talking to instruments: device-integration standards for self-driving laboratories
Year: 2026
Type: preprint
Abstract: Self-driving laboratories (SDLs) are rapidly outgrowing the single-instrument prototype, scaling into multi-instrument and multi-site platforms that run autonomous discovery across chemistry, materials science, biotechnology, pharmaceutical re-search and adjacent domains. What now decides whether an SDL can scale, share its integrations and federate experiments is no longer the algorithms or the robots, but the software layer that orchestrates heterogeneous devices. Today that layer is the bottleneck: a fast-growing ecosystem of open-source orchestrators in which most have built device integration on their own bespoke conventions instead of adopting a formally governed standard. The result is predictable and costly. Integrations do not transfer between laboratories, orchestrators cannot interoperate and many new SDLs rebuild their device-control layer from scratch. Crucially, this is an engineering coordination problem, not a fundamental scientific one and therefore among the most tractable bottlenecks in autonomous science. We map the device-integration standards landscape onto the open-source orchestrator ecosystem, compare the leading options and give scenario-driven guidance on when to use each. We then translate the requirements of autonomous operation into a concrete deliverable: a set of testable engineering criteria and a weighted procurement scorecard that defines an autonomous-ready control interface and gives institutional buyers the leverage to demand it; these requirements do not change as orchestration turns agentic: whether an instrument is driven by a program or by a language-model agent through a model-facing protocol, it is only as safe and reliable as the interface it exposes. Adopted at scale, these requirements turn device integration from a burden repaid lab by lab into shared infrastructure,
Source: ChemRxiv
Authors: Willi Gottstein, Lukas Bromig, Matthias Arnold, Sina Barazandeh, Joseph S Brown, Keith A. Brown, Ivano E. Castelli, Patrick Courtney, Basita Das, Martin Fitzner, Tobias Ginsburg, Aldair E. Gongora, Sang Soo Han, Annica Heyne, Georg Hinkel, Kedar Hippalgaonkar, Sergei V. Kalinin, Nicholas Kleiner, Christian Knittl-Frank, Jose Lugo-Martinez, Benji Maruyama, Pascal Miéville, AnkushKumar Mishra, Timothy Noël, Ozlem Ozcan, Noah Paulson, Ilja Porohovoj, Mark Z. Porter, Zekun Ren, Harmen M. van Rossum, Bastian Ruehle, Samuel Vitor Saraiva, Elia Savino, Philippe Schwaller, Woosuck Shin, Joshua W. Sin, Eric T. Sletten, Arthur Sloan, Tomasz M. Stawski, Casey Stone, Ryo Tamura, Dean Thomas, Tejs Vegge, Robert Waelder, Ilya Yakavets, Hyuk Jun Yoo, Naruki Yoshikawa, Sergio Pablo-García
Institutions: Carnegie Mellon University, Boston University, Technical University of Denmark, King's College London, Institute for Atomic and Molecular Physics, Merck KGaA, Darmstadt (Germany), Argonne National Laboratory, Lawrence Livermore National Laboratory, Seoul National University, Federal Institute For Materials Research and Testing, RheinMain University of Applied Sciences, Nanyang Technological University, University of Tennessee at Knoxville, United States Air Force Research Laboratory, University of Glasgow, École Polytechnique Fédérale de Lausanne, Iowa State University, University of Amsterdam, NCCR Catalysis, National Institute of Advanced Industrial Science and Technology, F. Hoffmann-La Roche AG, National Academies of Sciences, Engineering, and Medicine, National Institute for Materials Science, Lawrence Berkeley National Laboratory, Institute of Micro and Nanotechnology
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Award: G5567774746
OpenAlex ID: W7220677527