Friction on demand: A generative framework for the inverse design of metainterfaces

Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits their applicability to more complex or nonlinear friction laws. We introduce a generative modeling framework using Variational Autoencoders (VAEs) to infer surface topographies from target friction laws. Trained on a synthetic dataset composed of 200 million samples constructed from a parameterized contact mechanics model, the proposed method enables efficient, simulation-free generation of candidate topographies. We examine the potential and limitations of generative modeling for this inverse design task, focusing on balancing accuracy, throughput, and diversity in the generated solutions. Our results highlight trade-offs and outline practical considerations when balancing these objectives. This approach paves the way for near-real-time control of frictional behavior through tailored surface topographies. • Generative framework enables inverse design of frictional metainterfaces. • Variational autoencoders infer surface topographies from target friction laws. • Trained on a 200-million-sample synthetic Tribology dataset. • Model remains robust on out-of-distribution experimental friction laws. • Trade-offs between speed, accuracy, and solution diversity are quantified.