Designing Stealth Biomaterials


Over the last few decades, researchers have made significant advances in human-electronic interfaces, reprogramming DNA, sensing pico-molar concentration biomarkers, and biomedical implants. The outstanding challenge preventing these advances from impacting lives is their long-term durability in the human body. The body recognizes any interface with artificial materials as a wound and uses clotting or other methods to heal the interface. That process begins with the nonspecific adsorption of proteins, as seen on the left. Creating mechanically strong, durable, and biocompatible materials which prevent this process is the key to unlocking these novel technologies. In the White Lab, we're designing such materials using new computational methods to accelerate materials design.

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Creating Self-Assembling Materials


Self-assembly is the process of molecules spontaneously organizing into well-defined structure. The structures formed in self-assembly are highly sensitive to the molecular interactions and often result in self-similar hierarchical structures as shown on the left. Modeling and controlling self-assembly is a goal of our research, for both the design of materials and to increase our understanding of protein aggregation diseases. Rationally designed self-assembled materials can lead to more efficient organic polymer photovoltaics, better biomaterials, and high strength materials. Our unique approach to modeling self-assembly is to use experiments, simulations, and coarse-graining in a hierarchical multi-scale modeling approach.

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Better Materials for Sustainable Energy


The long-term vision of the White Lab is to create a set of computational tools to accelerate materials design. Custom designed and well-controlled materials are essential to the scientific progress of sustainable energy. For example, creating interfaces designed down to the atom in organic polymer photovoltaics is essential to create high-efficiency, printable, bendable solar-cells. Our approach to solving this challenge is to combine simulations and experiments at different scales. At a high-level, machine-learning is used to combine information from multiple experiments and sources into predictive models. On the more detailed level, the White Lab has designed a new class of chemical modeling techniques that allow direct incorporation of physics and experiments into a unified modeling framework.

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