Computationally Driven Design of Soft Materials

Mimicking the mechanical properties of soft biological tissues is vital for medical implants, tissue engineering, soft robotics, and wearable electronics. However, the unique combination of softness, strength, and toughness is difficult to recreate in synthetic materials. Current design strategies are predominantly Edisonian - exploratory mixing of assorted polymers, crosslinking schemes, and solvents, which is both inflexible in application and imprecise in property control. We develop a computationally driven approach for design of soft materials with solvent free network architecture. This approach is based on the theoretical and computational studies of the mechanical properties of networks made of graft polymers such as combs and bottlebrushes. In particular, it is demonstrated that the graft polymers in a melt behave as ideal chains with effective Kuhn length. Our analysis shows that the effective Kuhn length of the graft polymers is a universal function of the crowding parameter, describing overlap between neighboring macromolecules. This model of graft polymers is applied to model mechanical properties of networks of graft polymers in linear and nonlinear deformation regimes and to correlate network’s mechanical response with architectural and chemical structure of the network strands. This approach is verified by replicating elastic materials with mechanical properties of jellyfish, lung, and arterial tissue in PDMS networks of combs and bottlebrushes. This technique lays the foundation for a computationally driven materials design that will enable encoding of a broad range of mechanical properties of soft materials in solvent free elastomers.