Accelerating the Discovery of New Structural Adhesives

这个为期2年的项目的总体目标是开发方法，通过利用机器学习（ML）和机器人技术来加速发现新的结构粘合剂。这些战略旨在缓解与调查多组分制剂的手动方法相关的创新瓶颈。目前，研发管道需要多年时间来开发新的或改进的粘合剂。3M之所以支持这一拟议项目，是因为有兴趣提高其在价值超过650亿美元的全球粘合剂市场中快速开发新的和改进的粘合剂产品的能力。UBC的Berlinguette集团将在整个项目期间利用其独特的专业知识，将化学，机器人和机器学习相结合。第一年的项目目标包括设计“闭环”粘合剂配方工作流程和开发自动化样品制备系统。该项目的第二年将致力于整合这两个组件，并对粘合剂配方和表征闭环工作流程中的其他步骤进行自动化可行性研究。

The overarching goal of this 2-year project is to develop methodologies for accelerating the discovery of new structural adhesives by leveraging machine learning (ML) and robotics. These strategies seek to alleviate the innovation bottleneck associated with manual approaches to surveying multi-component formulations. Currently, R&D pipelines require multiple years to develop new or improved adhesives. 3M is supporting this proposed project due to an interest in increasing its capacity to rapidly develop new and improved adhesives products within the global adhesives market worth over US$ 65 billion. The Berlinguette Group at UBC will harness its unique expertise in combining chemistry, robotics, and machine learning throughout the duration of the project. Project objectives for Year 1 include designing a "closed-loop" adhesive formulation workflow and developing an automated sample preparation system. The second year of the project will be dedicated to integrating these two components and performing a feasibility study on automating additional steps in the closed-loop workflow for adhesive formulation and characterization.