Response-function-conditioned inverse design enables disorder as a design dimension in porous materials

Disordered porous materials offer broad mechanical-response spaces, but their non-periodic three-dimensional architectures make inverse design ill-posed: structurally distinct configurations can produce similar stress-strain curves, while scalar descriptors such as porosity cannot resolve the underlying topology. Here, we introduce a response-function-conditioned inverse design framework that learns directly from XCT-derived closed-cell aluminium-foam architectures and targets complete nonlinear stress-strain responses rather than individual properties. A point-cloud encoder and forward surrogate establish the structure-response relation, while conditional generation and gradient-based refinement search the learned structural space for manufacturable candidates. Trained on 844 XCT-derived subvolumes spanning porosities of 0.4034–0.8935, the forward model achieves a pointwise R² of 0.9918 and a median curve-level NRMSE of 3.015% on a parent-specimen-separated test set. For targets admitting multiple accepted solutions, inverse-designed candidates retain a median response NRMSE of 0.0358 while exhibiting a median pairwise structural distance of 0.2328, demonstrating non-trivial one-to-many design. Finite-element analyses and compression tests on additively manufactured architectures across three aluminium-alloy systems further confirm physical realizability. These results establish full-response conditioning as a practical route for designing real disordered porous materials beyond predefined unit cells.