Towards a modular architecture for science factories
Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories : large, general-purpose computation- and AI-enabled self-driving laboratories (SDLs) with the generality and scale needed both to tackle large discovery p...
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Published in | Digital discovery Vol. 2; no. 6; pp. 198 - 1998 |
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Main Authors | , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United Kingdom
Royal Society of Chemistry (RSC)
04.12.2023
|
Online Access | Get full text |
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Summary: | Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of
science factories
: large, general-purpose computation- and AI-enabled self-driving laboratories (SDLs) with the generality and scale needed both to tackle large discovery problems and to support thousands of scientists. Science factories require modular hardware and software that can be replicated for scale and (re)configured to support many applications. To this end, we propose a prototype modular science factory architecture in which reconfigurable
modules
encapsulating scientific instruments are linked with manipulators to form
workcells
, that can themselves be combined to form larger assemblages, and linked with distributed computing for simulation, AI model training and inference, and related tasks.
Workflows
that perform sets of actions on modules can be specified, and various
applications
, comprising workflows plus associated computational and data manipulation steps, can be run concurrently. We report on our experiences prototyping this architecture and applying it in experiments involving 15 different robotic apparatus, five applications (one in education, two in biology, two in materials), and a variety of workflows, across four laboratories. We describe the reuse of modules, workcells, and workflows in different applications, the migration of applications between workcells, and the use of digital twins, and suggest directions for future work aimed at yet more generality and scalability. Code and data are available at
https://ad-sdl.github.io/wei2023
and in the ESI.
Advances in robotic automation, high-performance computing, and artificial intelligence encourage us to propose large, general-purpose science factories with the scale needed to tackle large discovery problems and to support thousands of scientists. |
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Bibliography: | https://doi.org/10.1039/d3dd00142c Electronic supplementary information (ESI) available. See DOI AC02-06CH11357 USDOE |
ISSN: | 2635-098X 2635-098X |
DOI: | 10.1039/d3dd00142c |