Our laboratory has strong expertise in in-silico simulation, with density functional theory (DFT) and molecular dynamics (MD) as core tools. We have published numerous SCI-indexed papers on predicting the properties of industrial polymers, nanocomposites, and hybrid composite materials, with a particular focus on establishing structure–property relationships in condensed matter systems. Building on a solid background in mechanics, polymer physics, and computational materials science, we have been actively conducting collaborative research with leading specialty materials companies on the discovery and functional evaluation of eco-friendly advanced materials.

In response to growing technological demands, our recent research also includes low-dimensional nanomaterial–based thermal management materials, electrolyte design for supercapacitors and secondary batteries, and synthesis mechanisms of boron nitride nanostructures, using a combined approach of first-principles calculations and reactive molecular dynamics. Notably, our lab has been a pioneering group in integrating in-silico simulation with composite property prediction, proposing since 2008 inverse design concepts to predict the properties of matrices, reinforcements, and interface/interphase regions for multifunctional composites. This work has contributed to a paradigm shift in in-silico–driven design of multifunctional composite materials.

The ultimate goal of our in-silico research is to generate reliable, physics-based data for AI-driven materials design, enabling the construction of process–structure–property relationship models essential for manufacturing AI. To this end, we actively study reactive MD, classical MD, and coarse-grained MD to ensure efficient property prediction across multiple scales. Beyond academic research, we are pursuing tripartite collaborations with AI-based polymer design groups and major materials companies to translate these methodologies into technologies that can be directly applied in real industrial environments.