Arthi Jayaraman

Professor

Joint Professor, Materials Science & Engineering

Education 

• Post-Doctorate – 2008 University of Illinois, Urbana-Champaign
• Doctorate – 2006 North Carolina State University
• Masters – 2002 North Carolina State University
• Bachelors – 2000 Birla Institute of Technology and Science (BITS)

About Arthi Jayaraman

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 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 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. Jayaraman’s research has been recognized with honors/awards including ACS PMSE Fellow (2025), AIChE COMSEF IMPACT Award (2021), Fellow of the APS (2020), ACS PMSE Young Investigator (2014), AIChE COMSEF Young Investigator Award (2013), and Department of Energy (DOE) Early Career Research Award (2010).

Select Publications

For a complete listing of publications, please view the Full CV.

1. Modeling and simulations of polymers: a roadmap, TE Gartner III, A Jayaraman, Macromolecules 52 (3), 755-786
2. Machine Learning for Analyses and Automation of Structural Characterization of Polymer Materials,S Lu, A Jayaraman, Progress in Polymer Science, 101828
3. Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (“P(q) and S(q) CREASE”), CM Heil, Y Ma, B Bharti, A Jayaraman, JACS Au 3 (3), 889-904
4. Computational reverse-engineering analysis for scattering experiments (CREASE) with machine learning enhancement to determine structure of nanoparticle mixtures and solutions, CM Heil, A Patil, A Dhinojwala, A Jayaraman, ACS central science 8 (7), 996-1007
5. Polydispersity for Tuning the Potential of Mean Force between Polymer Grafted Nanoparticles in a Polymer Matrix, TB Martin, PM Dodd, A Jayaraman, Physical review letters 110 (1), 018301
6. Understanding the effect of polylysine architecture on DNA binding using molecular dynamics simulations, RM Elder, T Emrick, A Jayaraman, Biomacromolecules 12 (11), 3870-3879
7. Wetting–dewetting and dispersion–aggregation transitions are distinct for polymer grafted nanoparticles in chemically dissimilar polymer matrix, TB Martin, et al. Journal of the American Chemical Society 137 (33), 10624-10631
8. Computationally linking molecular features of conjugated polymers and fullerene derivatives to bulk heterojunction morphology, E Jankowski, HS Marsh, A Jayaraman, Macromolecules 46 (14), 5775-5785
9. Molecular dynamics simulation and PRISM theory study of assembly in solutions of amphiphilic bottlebrush block copolymers, I Lyubimov, MG Wessels, A Jayaraman, Macromolecules 51 (19), 7586-7599
10. Effect of peptide sequence on the LCST-like transition of elastin-like peptides and elastin-like peptide–collagen-like peptide conjugates: Simulations and experiments, A Prhashanna, PA Taylor, J Qin, KL Kiick, A Jayaraman, Biomacromolecules 20 (3), 1178-1189

Patents (h3)

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Awards

Research Areas

Jayaraman’s research lab develops and use molecular models, theory, simulations, and machine learning methods to design and characterize soft materials for a variety of applications. Currently, these are key research thrusts

1. Machine learning (ML) enabled automated analysis of materials’ characterization data
2. ML and Artificial Intelligence (AI) for solving chemical industry problems
3. Polymer, composites, & formulations
4. Bioderived, bioinspired, & biocompatible soft materials