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、高性能计算和人工智能的进展促使我们构想科学工厂：一种大规模、通用型、由计算与人工智能驱动的自驱动实验室，具备应对大型发现问题和支撑数千名科学家所需的通用性和规模。科学工厂需要可扩展的模块化硬件与软件，以便通过复制实现规模化，并能通过（重新）配置支持多种应用。为此，我们提出了一种模块化科学工厂原型架构，其中封装科学仪器的可重构模块通过机械臂连接形成工作单元，这些工作单元可进一步组合为更大系统，并与分布式计算相连以支持模拟、人工智能模型训练与推理及相关任务。该架构支持在模块上执行操作序列的工作流，并可并发运行由工作流及关联计算与数据处理步骤组成的多种应用。我们报告了在四间实验室中，基于15种不同机器人装置、五种应用（包含一项教育、两项生物、两项材料应用）及多种工作流对该架构进行原型验证的经验。我们描述了模块、工作单元及工作流在不同应用中的复用、应用在不同工作单元间的迁移、以及数字孪生的应用，并提出了面向更高通用性与可扩展性的未来研究方向。代码及数据见 https://ad-sdl.github.io/wei2023 及补充信息。