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

翻译：机器人自动化、高性能计算（HPC）和人工智能（AI）的进步促使我们构想科学工厂：大规模、通用计算与AI赋能的自主实验室（SDL），具备解决大型发现问题和支持数千名科学家所需的通用性和规模。科学工厂需要可复制的模块化硬件和软件，以实现规模扩展和（重）配置支持多种应用。为此，我们提出一种原型模块化科学工厂架构，其中封装科学仪器的可重构模块与机械臂连接形成工作单元，这些工作单元可进一步组合成更大的集合，并与分布式计算系统相连，用于仿真、AI模型训练和推理及相关任务。可指定在模块上执行动作序列的工作流，由工作流及相关计算和数据操作步骤组成的多种应用可并发运行。我们报告了该架构的原型构建经验，并在涉及15种不同机器人设备、五类应用（一项教育、两项生物学、两项材料科学）及多种工作流的四个实验室实验中应用。我们描述了不同应用中模块、工作单元和工作流的复用、应用在工作单元间的迁移以及数字孪生的使用，并为进一步提升通用性和可扩展性的未来工作提出方向。代码和数据见https://ad-sdl.github.io/wei2023及补充信息。