Computational Polymer Science & AI — developing mesoscale simulation models and machine learning approaches to discover novel polymer materials and understand soft matter physics.

▶ Research Area
1. Developing Mathematical Models for Complex Polymeric Systems
We develop coarse-grained simulation models and computational methods to study the self-assembly, thermodynamics, and dynamics of complex polymeric systems at the mesoscale.

2. Materials/Systems Design through Data Science and Machine Learning
We leverage artificial intelligence, machine learning, and data-driven approaches to accelerate the design of novel polymer materials and predict system behavior.

3. Non-simple Boundary and Its Effects on Self-assembly
We investigate how complex boundaries and confinement geometries influence the self-assembly of block copolymers and other soft matter systems.


Professor : Hur, Su-Mi
Simulation and Machine Learning for Soft Matter Lab Homepage