Machine intelligence-driven anti-curvature metamaterials for optimally enhanced specific strength, stiffness and energy absorption

Abstract

Innovation in the coupled design of unit-cell geometry and cell wall-level architecture can be crucial for multifunctional metamaterials, enabling simultaneous programming of stiffness, strength, and energy absorption beyond conventional straight-beam lattices. Here, we propose an anti-curvature strategy that retains the typical advantages of bending-dominated architectures, such as high energy absorption, while also improving effective stiffness and strength that are normally associated with stretching-dominated lattices. Building on this premise, we integrate physics-informed curvature-guided geometric design of ligament profiles with machine-intelligence for efficient predictive modelling. Nonlinear finite element simulations are performed to quantify the influence of curvature direction under different modes of loading, including compression, tension, and shear. The numerical results reveal significant curvature-dependent enhancements without any notable increase in weight: up to 51.56% in stiffness, 60.42% in onset-of-failure stress, and 57.80% in energy absorption under mode-dependent normal loading. Additionally, anti-curvature designs under shear can improve effective stiffness, onset-of-failure stress and energy absorption by up to 66.72%, 81.26% and 91.67%, respectively. The coupled design space of physics-informed cell-wall curvature and unit cell geometry, augmented by a machine learning-assisted efficient predictive modelling, provides a scalable route to engineer lightweight lattices with superior specific strength, tunable stiffness, and enhanced energy absorption for multifunctional applications.