Load Distributing Metamaterials Via Discrete Optimization

Mechanical metamaterials promise unprecedented control over load transfer by tailoring mesoscale architecture, yet most design strategies optimize global stiffness or energy absorption and only indirectly affect force redistribution. Here we introduce a computational–experimental framework to explicitly design load-distributing metamaterials by minimizing the variance of reaction forces transmitted to a support interface. Starting from a 3D face-centered cubic (FCC) beam lattice, we model deformation using a linear-elastic Timoshenko beam formulation and perform discrete topology optimization via Monte Carlo simulated annealing, where individual struts are selectively activated or removed. We demonstrate both single-objective optimization for a prescribed indenter position and a multi-objective formulation that enforces robust performance across multiple loading locations. Simulations show that optimized architectures transform highly localized support reactions into substantially more homogeneous force footprints, reducing the fraction of nearly unloaded nodes and capping peak forces. Stereolithography-printed elastomeric lattices validate these predictions: quasi-static pressure mapping reveals suppressed force hotspots and enlarged contact areas, while instrumented impact tests show earlier, more spatially distributed deformation and markedly improved repeatability–especially for off-center loading in multi-objective designs. This work establishes load homogenization as a primary, quantifiable design target for architected materials, enabling protective interfaces and supports with tunable and robust force-spreading behavior.