Polymers and Soft Matter / Machine Learning

The macroscopic properties of materials are largely determined by their microstructure, which often cannot be changed once made; and this limitation applies to most ‘hard’ materials. By contrast, soft biological materials such as proteins and tissues respond to external stimuli by changing the structure of constituent components and thus properties, which yield adaptive function often inaccessible by manmade materials. Underpinning this contrast is the basic component of biological materials – biomacromolecules with sequentially arranged monomers of prescribed physical and chemical properties. This points toward an opportunity for materials scientists: Can we design sequence-controlled polymers and exploit their self-assembly to create adaptive soft materials and use them to build devices with melded, adaptive function?

This research integrates four core aspects: (i) molecular design and automated synthesis, (ii) multi-scale assembly and mesoscopic structure, (iii) macroscopic nonlinear (mechanical, electric, magnetic, and optical) properties, and (iv) additive manufacturing. Integrating molecular theory, automated synthesis, advanced characterization, multi-scale modeling, and machine learning, we are accelerating the discovery of polymers with unusual, emergent properties and functions.

Current research projects include:

  • De novo sequence controlled bottlebrush polymers and networks
  • Additive manufacturing of soft materials and nanocomposites
  • High-performance solid-state polymer electrolytes for advanced battery technologies