ANISOTROPIC DIFFUSION BY QUADRATIC REGULARIZATION
openaire +1 more source
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir +4 more
wiley +1 more source
Deep Learning‐Assisted Design of Mechanical Metamaterials
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong +5 more
wiley +1 more source
This work details the rapid generation (t ≤ 5 min) of size‐tunable, ultralow dispersity (Ð ≤ 1.01) 2D hexagonal nanosheets by self‐limiting polymerization‐induced crystallization‐driven self‐assembly (SL‐PI‐CDSA) of modular and templating poly(aryl isocyanide) block copolymers, with functions that permit post‐polymerization modifications. Specifically,
Randall A. Scanga +13 more
wiley +2 more sources
Evolution of Physical Intelligence Across Scales
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu +7 more
wiley +1 more source
Composite B-spline regularized delta functions for the immersed boundary method: Divergence-free interpolation and gradient-preserving force spreading. [PDF]
Gruninger C, Griffith BE.
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Architecting Highly Anisotropic Thermal Conductivity in Flexible Phase Change Materials for Directed Thermal Management of Cylindrical Li-Ion Batteries. [PDF]
Chen L +7 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Quasi-anisotropic wet etching of glass creates inclined microstructures for advanced optical and MEMS devices. [PDF]
Yu J +9 more
europepmc +1 more source

