Deciphering and Translating Bioinspired Structures for Engineering Materials Design via Computational Modeling and Machine Learning

Nature has evolved extraordinary structural materials—such as nacre, bone, and the mantis shrimp’s dactyl club—that achieve remarkable combinations of strength, toughness, and impact resistance. These properties arise from sophisticated synergies between structure and composition. Inspired by these biological systems, this dissertation presents a comprehensive investigation into bioinspired materials, uncovering fundamental mechanisms and providing guidance on designing materials with superior mechanical properties. This dissertation begins by examining the "brick-and-mortar" structure of nacre, which informs the design of layered polymer-graphene nanocomposite films. Using coarse-grained molecular dynamics simulations, I elucidate mechanisms of dynamic wave propagation and energy dissipation in these systems, providing critical insights for the development of lightweight, impact-resistant structures. Inspired by the impact-resistant coating on the dactyl club of the mantis shrimp, I propose a novel class of nanoparticle-polymer nanocomposites that overcome the conventional stiffness-damping tradeoff. By introducing dynamic heterogeneity through nanoparticle reinforcement, these composites achieve simultaneous enhancements in stiffness and energy dissipation—offering transformative potential for protective materials and structural applications. Mammalian tissues are then introduced as a new inspiration for material design. The porous microarchitecture of bone is emulated through freeze-casting to produce biomimetic scaffolds. By integrating 3D printing, finite element modeling, and mechanics theory, I establish predictive relationships linking scaffold architecture to mechanical performance, enabling the rational design of bone-like scaffolds. Additionally, I investigate the tendon-bone insertion, a natural gradient material that seamlessly joins soft tendon and hard bone without leading to stress concentrations. Through analysis of its structure-composition-property relationships, I uncover mechanisms underlying its remarkable load transfer and damage tolerance, which inform the design of robust interfaces for engineering materials with dissimilar mechanical properties. Having mapped the pathways from structure and composition to mechanical function in biological materials, I then shift focus to inverse design—determining optimal structural and compositional configurations that yield desired mechanical properties. I demonstrate this approach using individual interface fibers at the bone end of the tendon–bone insertion. The mechanical behavior of these fibers depends on three spatial fields: mineralization scale, fibril angular dispersion, and mean fibril orientation. I develop a multiscale continuum model to predict fiber properties based on these inputs, generating a dataset used to train a convolutional neural network (CNN)-based surrogate model. This trained model enables an inverse design framework that integrates predictive modeling with gradient descent optimization. A case study validates the approach, demonstrating its efficacy in identifying optimal design parameters. This dissertation establishes a unified framework for the bioinspired design of next-generation structural materials. By uncovering the structure–function relationships of natural systems and leveraging data-driven inverse design techniques, I offer actionable pathways to engineer materials for advanced applications in aerospace, protective technologies, and biomedical devices.