Our primary research interest lies in understanding the complex behaviors of structural materials (e.g., thin films and composites) and soft matters (e.g., polymers and biomaterials) via bottom-up predictive modeling for material design, characterization and prediction of their performance in structure/infrastructure, energy and bioengineering applications.


To address these immediate challenges, we aims to develop multiscale materials-by-design framework – by integrating fundamental theories (i.e., soft matter physics, mechanics, continuum theories), computational techniques (i.e., molecular dynamics simulations, coarse-grained modeling, and machine learning algorithms), and experiments – to facilitate design and development of high-performance materials. In particular, we have developed a number of scale-bridging computational techniques to simulate polymers and soft matters at extended spatiotemporal scales for achieving quantitative prediction of their temperature-dependent behaviors (also known as the “temperature-transferable” issue). Utilizing these techniques, we have been able to extend our materials-by-design capabilities towards predicting the size-dependent dynamics and mechanical responses of thin films, nanocomposites, 2D materials, and bio-inspired materials under the influences of confinement and interfaces, which are broadly under the scopes of the Materials Genome Initiative (MGI) and the University’s Grand Challenge Initiative.