When machine learning meets 2D materials: a review

B Lu, Y **a, Y Ren, M **e, L Zhou, G Vinai… - Advanced …, 2024 - Wiley Online Library
The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …

Simple shear methodology for local structure–property relationships of sheet metals: State-of-the-art and open issues

G Han, J He, S Li, Z Lin - Progress in Materials Science, 2024 - Elsevier
Simple shear presents a local material structure–property relationship and plays an
important role in the development of material design, mechanical modeling, and …

[HTML][HTML] Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership

KL Snapp, B Verdier, AE Gongora, S Silverman… - Nature …, 2024 - nature.com
Energy absorbing efficiency is a key determinant of a structure's ability to provide
mechanical protection and is defined by the amount of energy that can be absorbed prior to …

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 …

Crack propagation simulation and overload fatigue life prediction via enhanced physics-informed neural networks

Z Chen, Y Dai, Y Liu - International Journal of Fatigue, 2024 - Elsevier
The fatigue crack growth simulation and life prediction of structures are implemented in this
paper based on the physics-informed neural networks (PINNs). Firstly, the enhanced PINNs …

Large language model agent as a mechanical designer

Y Jadhav, AB Farimani - arxiv preprint arxiv:2404.17525, 2024 - arxiv.org
Conventional mechanical design paradigms rely on experts systematically refining concepts
through experience-guided modification and FEA to meet specific requirements. However …

Machine learning applications in sheet metal constitutive Modelling: A review

AE Marques, TG Parreira, AFG Pereira… - International Journal of …, 2024 - Elsevier
The numerical simulation of sheet metal forming processes depends on the accuracy of the
constitutive model used to represent the mechanical behaviour of the materials. The …

Machine learning in solid mechanics: Application to acoustic metamaterial design

D Yago, G Sal‐Anglada, D Roca… - … Journal for Numerical …, 2024 - Wiley Online Library
Abstract Machine learning (ML) and Deep learning (DL) are increasingly pivotal in the
design of advanced metamaterials, seamlessly integrated with material or topology …

[HTML][HTML] Artificial Intelligence in Biomaterials: A Comprehensive Review

Y Gokcekuyu, F Ekinci, MS Guzel, K Acici, S Aydin… - Applied Sciences, 2024 - mdpi.com
The importance of biomaterials lies in their fundamental roles in medical applications such
as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with …

A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes

M Yin, Z Zou, E Zhang, C Cavinato… - Journal of the …, 2023 - Elsevier
Quantifying biomechanical properties of the human vasculature could deepen our
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …