Collaborative Research: NSF Workshop on Automated, Programmable and Self Driving Labs

合作研究：NSF 自动化、可编程和自动驾驶实验室研讨会

• 批准号: 2335910
• 负责人: Ian Foster
• 金额: $ 1.2万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335910&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; Workshop; Automated

Laboratory automation increases precision and efficiency of science experiments. Advances in low-cost sensors, actuators, robotic systems, and control systems have lowered the barrier to entry to laboratory automation such that fully self-driving labs will have the potential to enable new practices of science experiment and to accelerate scientific exploration progress. There is a critical need to develop the principles and methodologies for self-driving laboratories. These systems will likely draw from best practices and experiences learned in data science, human-machine interaction, manufacturing and quality control, open-source ecosystems, and laboratory science methods. The proposed workshop will convene leaders in self-driving laboratories and related areas including data science, robotics, manufacturing, and open-source ecosystems to define a roadmap for self-driving laboratories. Experts will discuss on latest advances and current challenges in automated sample preparation, experiment generation, data collection, and data analysis. The workshop will help identify major themes and assess on how future, interconnected goals can be best supported in a research context. By convening community leader around related topics, the workshop will seed cross-discipline collaboration on infrastructure that can support a broad range of sciences, which may include more complex experiments to accelerate scientific discovery and perform cost effective verification and validation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

实验室自动化提高了科学实验的精度和效率。低成本传感器，执行器，机器人系统和控制系统的进步降低了进入实验室自动化的障碍，因此完全自动驾驶实验室将有可能实现科学实验的新实践并加速科学探索进步。迫切需要开发自动驾驶实验室的原理和方法。这些系统可能会借鉴数据科学，人机互动，制造和质量控制，开源生态系统和实验室科学方法中学到的最佳实践和经验。拟议的研讨会将召集自动驾驶实验室和相关领域的领导者，包括数据科学，机器人技术，制造和开源生态系统，以定义自动驾驶实验室的路线图。专家将讨论自动样本准备，实验生成，数据收集和数据分析的最新进展和当前挑战。研讨会将有助于确定主要主题，并评估未来，互连目标在研究环境中如何得到最好的支持。 By convening community leader around related topics, the workshop will seed cross-discipline collaboration on infrastructure that can support a broad range of sciences, which may include more complex experiments to accelerate scientific discovery and perform cost effective verification and validation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.