Our research interests lie in the development and use of molecular models, theory, simulations, and machine learning to design new and improved materials for a variety of applications. In most of our projects our focus is on macromolecules (i.e., synthetic or biologically relevant polymers) and their structure, dynamics, and thermodynamics when mixed with solvents, nanoparticles, colloids, etc. We work closely with experimentalists to validate our approaches, test our predictions, and guide the next experiment.
Research Focus Areas
- Polymers – Synthetic & Bioinspired/Bioderived
- Soft Materials – Colloids, Nanoparticle-Polymer Mixtures
- Material Design-Structure-Properties Relationships
- Molecular Modeling & Simulation
- Machine Learning
- Computational Method Development
NSF-NRT Graduate Traineeship on Computing and Data Science for Soft Materials
Prof. Arthi Jayaraman is the director for a UD based graduate traineeship funded by NSF on Computing and Data Science Training for Soft Materials Innovation, Discovery, and Analytics.
Interested PhD students should take a look at the NRT trainee experiences .
Recorded Videos of Research Talks from the Group
If you are interested you can check out some of our recent conference presentation talks Jayaraman Research Group on Youtube
Open Source Codes Developed by Members of Our Lab
- PRISM theory package developed by former Jayaraman lab members Tyler Martin and Tom Gartner with Ron Jones and Chad Synder at NIST: pyPRISM package
- CREASE method for analysis of scattering results: CREASE-GA
- Self-supervised machine learning model for analysis of from transmission electron microscopy images from nanomaterials: https://github.com/arthijayaraman-lab/self-supervised_learning_microscopy_images
References for Theory & Simulation Methods We Use
- Modeling and Simulation of Polymers: A Roadmap by T. Gartner and A. Jayaraman
- Polymer Reference Interaction Site Model (PRISM) theory by K. S. Schweizer and J. Curro
- Paper about the pyPRISM Tool developed by Martin, Gartner, Jones, Synder and Jayaraman
Relevant Books
- Allen, M.P. and Tildesley, D.J. (1987). Computer Simulation of Liquids. Oxford University Press. ISBN 0-19-855645-4.
- Frenkel, D. and Smit, B. (2001). Understanding Molecular Simulation. Academic Press. ISBN 0-12-267351-4.
- Binder, K. and Heermann, D.W. (2002). Monte Carlo Simulation in Statistical Physics. An Introduction (4th edition). Springer. ISBN 3-540-43221-3.
