Machine learning based analyses and interpretation of structural characterization data
from soft materials, Prof. Arthi Jayaraman
1Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE
2Department of Materials Science and Engineering, University of Delaware, Newark, DE
3Data Science Institute, University of Delaware, Newark, DE
My research group specializes in developing physics-based molecular models and simulation
methods, as well as data-driven machine learning models, for designing and characterizing soft
macromolecular materials. In recent years, we have dedicated significant efforts to creating
machine learning–based computational methods that accelerate and automate the interpretation
of structural characterization data from scattering and microscopy techniques. In this talk, I will
highlight several examples to showcase our recent work (e.g., CREASE [1-4], PairVAE [5],
microscopy analyses [6,7]). I will explain the key features of these methods and how we apply
them to experimental data shared by our collaborators to establish structure-property
relationships across a broad range of soft materials.
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] R. Adhikari et al., J. Appl. Cryst, 58, 1384–1398 (2025)
[5] S. Lu and A. Jayaraman, JACS Au 3, 9, 2510–2521 (2023)
[6] A. Paruchuri et al., Digital Discovery, 3, 2533-2550 (2024)
[7] S. Lu and A. Jayaraman, Progress in Polymer Science 153, 101828 (2024)
Users interested in the open-source codes can access them here: https://github.com/arthijayaraman-lab
Biography:
Arthi Jayaraman is currently a full professor in the Departments of Chemical and
Biomolecular Engineering and Materials Science and Engineering at the University of Delaware
(UD), Newark. She also directs an NSF-funded NRT graduate traineeship program on ‘Computing
and Data Science Training for Materials Innovation, Discovery, and Analytics’. She serves as an
associate editor for Macromolecules and was the inaugural deputy editor for the first three years
of ACS Polymers Au.
Jayaraman earned her Ph.D. in Chemical Engineering from North Carolina State
University and completed her postdoctoral research in Materials Science and Engineering at the
University of Illinois at Urbana-Champaign. After serving as the Patten Assistant Professor in the
Department of Chemical and Biological Engineering at the University of Colorado (CU) Boulder,
she joined UD faculty in 2014. Her research focuses on developing and applying computational
techniques to study polymer nanocomposites, blends, solutions, and biomaterials.
Her honors include the American Chemical Society (ACS) PMSE Fellowship (2024), UD
College of Engineering Faculty Award for Excellence in Teaching (2023), AIChE COMSEF Impact
Award (2021), American Physical Society (APS) Fellowship (2020), Dudley Saville Lectureship at
Princeton University (2016), ACS PMSE Young Investigator (2014), AIChE COMSEF division
Young Investigator Award (2013), CU Provost Faculty Achievement Award (2013), Department of
Energy (DOE) Early Career Research Award (2010), and CU Department of Chemical and
Biological Engineering’s outstanding undergraduate teaching award (2011) and graduate
teaching award (2014).