We are a curiosity-driven computational research group in the Macromolecular Science & Engineering Department.

The group is interested in problems at the intersection of Engineering, Physics, Chemistry, Material Science, Computation, and Machine Learning. We develop multi-scale simulation schemes and use machine learning to build up predictable constitutive models that can answer questions like:

1. How and why do the particulate systems, like concrete, battery electrolytes, polymers, etc., behave the way they do?

2. Can we use simulations to understand experimental behavior?

3. How do we relate the introductory microscale physics and chemistry to tune/manipulate the bulk response of the materials?

4. How to use and develop tools from network science and complex system theory to understand the flow and dynamics of biological, industrial, and natural systems?