Deep learning in mechanical metamaterials: from prediction and generation to inverse design

X Zheng, X Zhang, TT Chen, I Watanabe - Advanced Materials, 2023 - Wiley Online Library
Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …

[HTML][HTML] Biomimetic scaffolds using triply periodic minimal surface-based porous structures for biomedical applications

R Pugliese, S Graziosi - SLAS technology, 2023 - Elsevier
The design of biomimetic porous scaffolds has been gaining attention in the biomedical
sector lately. Shells, marine sponges, shark teeth, cancellous bone, sea urchin spine, and …

Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling

L Zheng, K Karapiperis, S Kumar… - Nature …, 2023 - nature.com
The rise of machine learning has fueled the discovery of new materials and, especially,
metamaterials—truss lattices being their most prominent class. While their tailorable …

Machine learning predictions on the compressive stress–strain response of lattice-based metamaterials

L **ao, G Shi, W Song - International Journal of Solids and Structures, 2024 - Elsevier
Predicting the stress–strain curve of lattice-based metamaterials is crucial for their design
and application. However, the complex nonlinear relationship between the mesoscopic …

Inverse design of 3D cellular materials with physics-guided machine learning

M Abu-Mualla, J Huang - Materials & Design, 2023 - Elsevier
This paper investigates the feasibility of data-driven methods in automating the engineering
design process, specifically studying inverse design of cellular mechanical metamaterials …

[HTML][HTML] Inverse-designed growth-based cellular metamaterials

S Van't Sant, P Thakolkaran, J Martínez, S Kumar - Mechanics of Materials, 2023 - Elsevier
Advancements in machine learning have sparked significant interest in designing
mechanical metamaterials, ie, materials that derive their properties from their inherent …

Machine learning–enabled inverse design of shell-based lattice metamaterials with optimal sound and energy absorption

Z Hu, J Ding, S Ding, WWS Ma, JW Chua… - Virtual and Physical …, 2024 - Taylor & Francis
Currently, the development in shell-based lattice, is increasingly focused on
multifunctionality, with growing interest in combining sound and energy absorption …

[HTML][HTML] Design of a composite metamaterial toward perfect microwave absorption and excellent load-bearing performance

Z Zhu, J Zhou, Y Li, X Qi, Y Wang, Y Wen - Materials & Design, 2023 - Elsevier
In this paper, an ultrathin layer of arrayed electromagnetic resonators is introduced on the
CFRP laminate to form a meta-CFRP composite. It is quite an exciting and promising design …

A review of graph neural network applications in mechanics-related domains

Y Zhao, H Li, H Zhou, HR Attar, T Pfaff, N Li - Artificial Intelligence Review, 2024 - Springer
Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …

Formation energy prediction of crystalline compounds using deep convolutional network learning on voxel image representation

A Davariashtiyani, S Kadkhodaei - Communications Materials, 2023 - nature.com
Emerging machine-learned models have enabled efficient and accurate prediction of
compound formation energy, with the most prevalent models relying on graph structures for …