Goodrich Group

Theoretical and Computational Soft Matter

The Goodrich Group is interested in how vast design spaces appearing in many soft matter systems give rise to rich, emergent, and controllable behavior. Such design spaces enable emergent complexity in machine learning and evolution, but how can they be exploited in material systems to rationally design and create new technologies? We aim to uncover general physical principles that govern structure, dynamics, and physical learning in systems ranging from disordered solids to programmable nanomaterials. By identifying universal mechanisms that generalize across specific materials or scales, we hope to advance both fundamental physics and the rational engineering of complex matter, enabling new forms of self-assembly, adaptability, and biomimetic functionality in real materials and devices.

Our approach is to combine computational and theoretical tools to discover basic soft matter principles that could one day lead to new functional materials as well as deepen our understanding of complex matter. We also develop and apply new methodologies such as differentiable molecular dynamics and gradient-based optimization over path ensembles, allowing us to directly connect microscopic dynamics to emergent behavior and design objectives. Current efforts span programmable self-assembly, inverse design in disordered systems, physical learning, and functional nanomachines. Together, these threads aim to build a unified framework for understanding and engineering matter that can organize, compute, and act with purpose.