When machine learning meets 2D materials: a review
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 …
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
Simple shear presents a local material structure–property relationship and plays an
important role in the development of material design, mechanical modeling, and …
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
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 …
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
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 …
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 …
paper based on the physics-informed neural networks (PINNs). Firstly, the enhanced PINNs …
Large language model agent as a mechanical designer
Conventional mechanical design paradigms rely on experts systematically refining concepts
through experience-guided modification and FEA to meet specific requirements. However …
through experience-guided modification and FEA to meet specific requirements. However …
Machine learning applications in sheet metal constitutive Modelling: A review
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 …
constitutive model used to represent the mechanical behaviour of the materials. The …
Machine learning in solid mechanics: Application to acoustic metamaterial design
Abstract Machine learning (ML) and Deep learning (DL) are increasingly pivotal in the
design of advanced metamaterials, seamlessly integrated with material or topology …
design of advanced metamaterials, seamlessly integrated with material or topology …
[HTML][HTML] Artificial Intelligence in Biomaterials: A Comprehensive Review
The importance of biomaterials lies in their fundamental roles in medical applications such
as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with …
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
Quantifying biomechanical properties of the human vasculature could deepen our
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …