Postdoctoral Fellowship: MPS-Ascend: Coarse-grained Modeling of Melanin-mediated Structural Color

About This Grant

NONTECHNICAL SUMMARY Color is all around us—in clothing, art, nature, and everyday objects—and it has shaped human history for thousands of years. Most colors we see come from chemical dyes and pigments that absorb certain wavelengths of light. But in nature, some of the most stunning colors—like those on butterfly wings or bird feathers—come from tiny structures that bend and scatter light, not from pigments. This is called structural color. This project explores how to recreate those natural effects using materials that mimic the way bird feathers produce color. By experimenting with tiny particles and the materials that hold them together, the research team hopes to learn how different structures create different colors. Using computer simulations and machine learning, they will develop tools to design new materials that show color without using dyes. This work could lead to more sustainable, long-lasting colors for everything from clothing to cosmetics—no fading, no chemicals. The research team will also share their findings through a public science blog and educational events to make this exciting science more accessible to everyone. TECHNICAL SUMMARY This project focuses on computational research on biomimetic melanin-based structural color. In nature, melanin nanoparticles enmeshed in a keratin matrix can impart a variety of colors. Experimentalists have been able to reproduce this effect using mixtures of melanin and silica nanoparticles. In this project, the PI will use physics- and chemistry-based computational tools to study how the keratin matrix affects the structures formed and resultant optical properties. Work will proceed along three incremental steps: (1) development of a coarse-grained model that captures the self-assembly of different melanin-particle mixtures in the keratin matrix, enabling the creation of a preliminary dataset correlating design parameters to the resultant structure and optical properties; (2) validation of the computational results with experimentally-fabricated structures, allowing for model recalibration/optimization; and (3) using the results of steps 1-2, creating a forward machine learning model to pinpoint the parameters that will yield tailored optical properties. This project will both enable advanced training for members of the research team, and add to the suite of freely available computational tools for the design and manufacture of structural color. The developed tools will be made publicly available to aid other researchers. Finally, the PI will also communicate the results of this work and other scientific ventures to the public (at a level accessible to non-scientists) through a dedicated science blog and STEM education conferences. STATEMENT OF MERIT REVIEW This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.