Structural characterization of polymer materials is a major step in the process of creating design-structural-property relationships. With growing interests in artificial intelligence (AI)-driven materials design and high-throughput synthesis and measurements, there is a critical need for development of complementary data-driven approaches (e.g., machine learning models and workflows) to enable fast and automated interpretation of the characterization data. In this talk I will share the needs for machine learning specifically in the context of two commonly used structural characterization techniques for polymer materials: microscopy and scattering. [1] I will share recent work from my lab focused on development and application of machine learning models / workflows for these types of measurements. [e.g., References 2-5] I will end with my perspective on potential opportunities to successfully integrate such data-driven methods with high-throughput experimentation in the field of polymer science.

References
[1] S. Lu & A. Jayaraman, Progress in Polymer Science, 153, 101828 (2024)
[2]. Open-source codes available at https://github.com/arthijayaraman-lab
[3] C. M. Heil, et al., ACS Central Science 8, 7, 996-1007 (2022).
[4] C. M. Heil et al., JACS Au 3, 3, 889–904 (2023).
[5] S.V.R. Akepati et al., JACS Au 4, 4, 1570–1582 (2024).