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 funded research projects
- Machine learning-guided discovery of DNA-stabilized silver nanoclusters as NIR fluorophores for deep tissue imaging
- DNA-stabilized silver nanocluster probes for super-resolution bioimaging
- Atomically precise materials engineering enabled by DNA nanotechnology
- Near-field effects in nanocluster architectures
- Block copolymer nanomaterials as a route to multi-length scale assembly
Additional projects / completed projects
- Metal-mediated DNA base pairing to expand the code of DNA nanotechnology
- Machine learning and data mining for informed design of biomaterials