Green Research Group

While we can synthesize a vast number of molecular structures at small scales, it is difficult to quantitatively control the nonequilibrium processes required to build functional supramolecular systems at nanometer and micron scales. However, living systems show that flows of matter and energy can organize multiscale materials, drive motion, regulate reaction networks, and replicate structure. Our research aims to understand and design synthetic systems that convert chemical energy into controlled, energy-efficient dynamical behaviors. The problems in this direction have an overarching question: how do nonequilibrium flows of matter and energy set the limits and design rules for dynamical function?

To address this question, we develop theory and computational techniques for synthetic and biological materials—such as self-assembling structures and active materials—that use reaction and transport processes to assemble, sustain, and reorganize structure or generate work on targeted timescales. We build coarse-grained models grounded in statistical mechanics, nonlinear dynamics, stochastic thermodynamics, and data-driven inference that quantify tradeoffs between speed, accuracy, stability, and energy use and infer mechanisms from experimental data. Our goal is to deliver predictive frameworks that guide experiments and enable the rational design of energy- efficient synthetic and biological materials.