Linking computational techniques to experiments for design-structure-property relationships in soft materials

Lecture / Panel
For NYU Community



Arthi Jayaraman

Centennial Term Professor for Excellence in Research and Education
Dept. of Chemical and Biomolecular Engineering 
(Joint appointment in) Dept. of Materials Science and Engineering
Affiliated faculty, Data Science Institute 
University of Delaware, Newark DE



Jayaraman research group’s expertise lies in development of coarse-grained models and computational approaches involving liquid state theory, molecular simulation, and machine learning for designing and characterizing soft macromolecular materials. In this talk, I will present ‘Computational Reverse Engineering of Scattering Experiments (CREASE)’, a computational method that my group members have recently developed for analysis of small angle scattering profiles and interpretation of the structure in soft materials. [e.g., Refs.1-5] We have applied CREASE to experimental small angle X-ray and neutron scattering profiles obtained from different soft materials (e.g., methylcellulose fibrillar structures, micelles in amphiphilic polymer solutions, segregation in binary nanoparticle mixtures) to test various hypotheses regarding the domain shapes and sizes within the structure and calculate the relevant structural dimensions. CREASE has been useful to interpret structural detail at a range of length scales for soft materials without relying on fitting with off-the-shelf analytical models that may be too approximate for novel polymers and/or unconventional assembled structures. In recent work [6-7] we have also demonstrated how one can take CREASE’s structural interpretation as an input for other computational methods that predict macroscopic properties (e.g., color, reflectance profiles) thus serving as a valuable tool for predicting structure-property relationships. Interested researchers can find more about CREASE on and

Computational Reverse-Engineering Analysis for Scattering Experiments’ (CREASE)


Arthi Jayaraman is currently Centennial Term Professor for Excellence in Research and Education in the Departments of Chemical and Biomolecular Engineering and Materials Science and Engineering at the University of Delaware (UD), Newark. She is currently the director for an NSF-funded NRT graduate traineeship program on ‘Computing and Data Science Training for Materials Innovation, Discovery, and Analytics’. She also serves as editor for both American Chemical Society (ACS) journals Macromolecules and ACS Polymers Au (gold). Jayaraman received her Ph.D. in Chemical Engineering from North Carolina State University and conducted her postdoctoral research in Materials Science and Engineering at University of Illinois-Urbana Champaign. After holding the position of Patten Assistant Professor in the Department of Chemical and Biological Engineering at University of Colorado (CU) at Boulder, in 2014 she joined the faculty at UD. Jayaraman’s research expertise is in development of molecular modeling, theory and simulation techniques and application of these techniques to study polymer nanocomposites, blends, and solutions, and biomaterials. She has received the following honors: 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).