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 …

Recent advances and applications of machine learning in experimental solid mechanics: A review

H Jin, E Zhang, HD Espinosa - Applied …, 2023 - asmedigitalcollection.asme.org
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

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 …

Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models

JH Bastek, DM Kochmann - Nature Machine Intelligence, 2023 - nature.com
The accelerated inverse design of complex material properties—such as identifying a
material with a given stress–strain response over a nonlinear deformation path—holds great …

[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 …

AI-enhanced biomedical micro/nanorobots in microfluidics

H Dong, J Lin, Y Tao, Y Jia, L Sun, WJ Li, H Sun - Lab on a Chip, 2024 - pubs.rsc.org
Human beings encompass sophisticated microcirculation and microenvironments,
incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in …

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

J Lee, D Park, M Lee, H Lee, K Park, I Lee, S Ryu - Materials Horizons, 2023 - pubs.rsc.org
In the last few decades, the influence of machine learning has permeated many areas of
science and technology, including the field of materials science. This toolkit of data driven …

Perspective: Machine learning in design for 3D/4D printing

X Sun, K Zhou, F Demoly… - Journal of Applied …, 2024 - asmedigitalcollection.asme.org
Abstract 3D/4D printing offers significant flexibility in manufacturing complex structures with
a diverse range of mechanical responses, while also posing critical needs in tackling …

Tunable Multifunctional Metamaterial Sandwich Panel with Quasi‐Zero Stiffness Lattice Cores: Load‐Bearing, Energy Absorption, and Vibration Isolation

W Liu, L Wu, J Sun, J Zhou - Advanced Materials Technologies, 2024 - Wiley Online Library
The interest in novel mechanical metamaterials that offer advanced functionalities and
mechanical tunability for highly compatible multifunctional performance in state‐of‐the‐art …

Mechanical metamaterials fabricated from self-assembly: A perspective

H Jin, HD Espinosa - Journal of Applied Mechanics, 2024 - asmedigitalcollection.asme.org
Mechanical metamaterials, whose unique mechanical properties stem from their structural
design rather than material constituents, are gaining popularity in engineering applications …