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 pr...
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Main Authors | , , , , , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
18.08.2023
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Subjects | |
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 Supplementary Information |
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DOI: | 10.48550/arxiv.2308.09793 |