We used this model to inversely design spinodal metamaterials for large deformations, validated by exp. on the optimized designs. This work aids in understanding structure-property relations of high-density spinodals at finite strains, relevant to energy absorption applications.

The experimental data served to develop a physics-enhanced deep learning framework that leverages multiple convex energy potentials to provide a nonconvex potential, whose derivative yields the nonlinear stress-strain responses of spinodal metamaterials.

We used >300 large-deformation experiments on samples spanning the design space, accompanied by nonlinear computational models, to gain insight on the deformation and energy dissipation mechanisms towards enhanced structure-property relations in this nonlinear regime.