Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

Text-to-Microstructure Generation Using Generative Deep Learning

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human-computer interaction. In this study, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design and discovery.

Three-Dimensional Optical Imaging of Internal Deformations in Polymeric Microscale Mechanical Metamaterials

Recent advances in two-photon polymerization fabrication processes are paving the way to creating macroscopic metamaterials with microscale architectures, which exhibit mechanical properties superior to their bulk material counterparts. These metamaterials typically feature lightweight, complex patterns such as lattice or minimal surface structures. Conventional tools for investigating these microscale structures, such as scanning electron microscopy, cannot easily probe the internal features of these structures, which are critical for a comprehensive assessment of their mechanical behavior. In turn, we demonstrate an optical confocal microscopy-based approach that allows for high-resolution optical imaging of internal deformations and fracture processes in microscale metamaterials under mechanical load. We validate this technique by investigating an exemplary metamaterial lattice structure of 80 × 80 × 80 μm3 in size. This technique can be extended to other metamaterial systems and holds significant promise to enhance our understanding of their real-world performance under loading conditions.

Superior Strength, Toughness, and Damage-Tolerance Observed in Microlattices of Aperiodic Unit Cells

Characterized by periodic cellular unit cells, microlattices offer exceptional potential as lightweight and robust materials. However, their inherent periodicity poses the risk of catastrophic global failure. To address this limitation, a novel approach, that is to introduce microlattices composed of aperiodic unit cells inspired by Einstein's tile, where the orientation of cells never repeats in the same orientation is proposed. Experiments and simulations are conducted to validate the concept by comparing compressive responses of the aperiodic microlattices with those of common periodic microlattices. Indeed, the microlattices exhibit stable and progressive compressive deformation, contrasting with catastrophic fracture of periodic structures. At the same relative density, the microlattices outperform the periodic ones, exhibiting fracture strain, energy absorption, crushing stress efficiency, and smoothness coefficients at least 830%, 300%, 130%, and 160% higher, respectively. These improvements can be attributed to aperiodicity, where diverse failure thresholds exist locally due to varying strut angles and contact modes during compression. This effectively prevents both global fracture and abrupt stress drops. Furthermore, the aperiodic microlattice exhibits good damage tolerance with excellent deformation recoverability, retaining 76% ultimate stress post-recovery at 30% compressive strain. Overall, a novel concept of adopting aperiodic cell arrangements to achieve damage-tolerant microlattice metamaterials is presented.