A bioinspired approach to photonics and electronics
Biology builds photonic systems out of soft matter, with individual components programmed for bottom-up assembly of remarkably complex structures. Understanding and achieving this kind of assembly in manmade systems is our goal. We are interested in how macromolecules assemble complex ordered structures, and we seek to use these principles to build novel photonic and electronic materials that exploit soft matter’s programmable assembly.
Machine learning and data mining for materials discovery
A data-driven approach to outstanding questions in soft matter is a crosscutting theme of our research. We combine machine learning and data mining with high-throughput experiments to better learn from our data, inform first-principles models, and design new photonic materials.
Current research projects
- DNA-stabilized silver clusters – fluorophores with a “genome” for applications in NIR bioimaging and photonics
- Metal-mediated DNA base pairing to expand the code of DNA nanotechnology
- Block copolymer nanomaterials as a route to multi-length scale assembly
- Machine learning and data mining for informed design of biomaterials