Events

My research group’s expertise lies in the development of physics-based molecular models and simulation methods as well as data-driven, machine learning (ML) models for designing and characterizing soft macromolecular materials. Half of my group devotes considerable time and effort towards developing ML-based computational methods to accelerate and automate interpretation of complex structural characterization data from small-angle scattering and microscopy techniques. In this talk, I will highlight a few examples to showcase some of our recent work along these lines. For the methods I will highlight, first, I will motivate the need for those methods in the soft materials field. I will also share key steps in the workflow and how we apply those methods on experimental data shared by our collaborators to establish structure-property relationships for a broad range of soft materials.

Users interested in these methods can find our open-source codes here: https://github.com/arthijayaraman-lab

Arthi Jayaraman is currently a tenured professor in the Departments of Chemical and Biomolecular Engineering and Materials Science and Engineering at the University of Delaware (UD), Newark. She is also the director for an NSF-funded NRT graduate traineeship program on ​‘Computing and Data Science Training for Materials Innovation, Discovery, and Analytics.’ She currently serves as associate editor for Macromolecules and for the last three years also served as the inaugural deputy editor for ACS Polymers Au. She received her Ph.D. in Chemical Engineering from North Carolina State University and conducted her postdoctoral research in Materials Science and Engineering at the University of Illinois-Urbana Champaign.

Her research expertise is in the development and application of physics-/chemistry-based and data-driven modeling to study polymers, nano- and bio-materials. Her excellence in research and teaching have been recognized with many honors including  American Chemical Society (ACS) PMSE Fellowship (2025), UD College of Engineering Faculty Award for Excellence in Teaching (2023), AIChE COMSEF IMPACT Award (2021), American Physical Society (APS) Fellowship (2020), ACS PMSE Young Investigator (2014),  AIChE COMSEF division Young Investigator Award (2013), University of Colorado (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).