Glycomaterials are an emerging class of hybrid materials created by integrating monosaccharides or oligosaccharides with synthetic building blocks, holding immense potential across diverse fields ranging from biomedical technologies to advanced adhesives. The extensive structural diversity of carbohydrates and synthetic components provides exceptional opportunities for precise tailoring of material properties. However, their broader development and application are severely limited by insufficient fundamental understanding of glycomaterial behavior. This knowledge gap stems primarily from a lack of robust multi-scale computational models. Beyond establishing molecular-level insights, combining these computational models with data-driven approaches will enable efficient and systematic exploration of the glycomaterial design space, driving the rational design and optimization of glycomaterials with targeted properties. In this talk, I will highlight our group's efforts in developing transferable coarse-grained models and employing advanced sampling methods to study glycomaterial solutions, architectures, and interfaces. Fundamental insights from these simulations have significantly advanced our understanding of glycomaterial behavior, enabling the rational design of novel glycomaterials with precisely tailored binding affinities and solubilities through the integration of data-driven frameworks. Several computationally predicted glycomaterials are currently undergoing experimental validation. Our integrated computational-experimental framework thus offers a powerful strategy for systematically engineering glycomaterials with tailored properties, selective interactions, and improved performance.
