My research group’s expertise lies in the development of physics-based molecular models and simulation methods as well as data-driven machine learning models for designing and characterizing soft macromolecular materials. In the past 5 years we have devoted significant efforts towards the development of machine learning based computational methods to accelerate and automate interpretation of structural characterization data from scattering and microscopy techniques. In this talk, I will highlight a few examples to showcase our recent work (e.g., CREASE [1-3], PairVAE [4], microscopy analyses [5,6]). I will describe the key features of these methods and how we use them on experimental data shared by our collaborators to establish structure-property relationships for a broad range of soft materials.

Users interested in the open-source codes can access them here: https://github.com/arthijayaraman-lab

References

[1] C. M. Heil et al.,  ACS Central Science 8, 7, 996-1007 (2022).

[2] C. M. Heil et al., JACS Au 3, 3, 889–904 (2023). 

[3] S.V.R. Akepati et al., JACS Au  4, 4, 1570–1582 (2024).

[4] S. Lu and A. Jayaraman, JACS Au 3, 9, 2510–2521 (2023). 

[5] A. Paruchuri et al., Digital Discovery, 3, 2533-2550 (2024)

[6] S. Lu and A. Jayaraman, Progress in Polymer Science 153, 101828 (2024)