Accelerating the Discovery of New Structural Adhesives

点击翻译按钮获取中文摘要

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.