Cellular Material Network: A General Machine Learning Architecture for Predicting Mechanical Properties of Cellular Materials

Abstract

Cellular materials with porous and lightweight structures have attracted attention due to exceptional mechanical tunability and broad applications. Despite advancements in design strategies, traditional simulations remain computationally intensive, motivating developments of efficient optimization methodologies. This study introduces Cellular Material Network (CM‐Net), a physics‐informed machine learning architecture for predicting mechanical properties of cellular materials. By treating basic geometric units as tokens analogous to those in natural language processing, CM‐Net achieves efficient forward prediction and generalization across diverse structures and compositions. Validated against simulations and experiments, CM‐Net accurately predicts nonlinear behaviors, including initial peak compression force, mean compression force, and energy absorption. The model maintains low relative errors for out‐of‐distribution scenarios such as varying wall thicknesses and novel topologies composed of short straight segments. However, extrapolation accuracy decreases for geometries with long curved edges, owing to insufficient representation of deformation mechanisms in training. CM‐Net also predicts the behavior of composite and gradient structures, as well as nonlinear force–displacement curves. We further demonstrate inverse designs of complex irregular cellular materials to meet specific requirements, illustrating CM‐Net's utility as a design assistant, with experimental validation confirming its practical applicability. Its scalability and generalizability make CM‐Net a transformative tool for accelerating lightweight, high‐performance cellular material development.