Our primary research interest lies in understanding the complex behaviors of soft materials via bottom-up predictive modeling for material design, characterization, and prediction of their performance in structure/infrastructure, energy and sustainability, and bioengineering applications. Our group aim to develop a multiscale materials-by-design framework – by integrating fundamental theories (i.e., soft matter physics, mechanics, continuum theories), computational techniques (i.e., molecular dynamics, coarse-grained modeling, and machine learning), and experiments – to facilitate design and development of high-performance materials.

Our group have developed a number of scale-bridging computational techniques and extend our materials-by-design capabilities for hierarchical material systems, including thin films, nanocomposites, 2D materials, and bio-inspired materials, 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 (CECT).

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