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 aim 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 The Center for Engineered Cancer Test Beds.