Results 211 to 220 of about 225,487 (278)

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Miniature 3D-printed rod-like refractive objective for endoscopic applications. [PDF]

J Biomed Opt
Beckford K, Ma Y, Liu J, Tkaczyk TS.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

High-Pressure Synthesis of the First Thermodynamically Stable Silver Nitride, AgN₅. [PDF]

JACS Au
Liang A, Ten Eikelder HRA, Ranieri U, Spender J, Massani B, Fedotenko T, Glazyrin K, Giordano N, Lawrence Bright E, Wright J, Shi LT, Trybel F, Laniel D.   +12 more
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Accelerated first-order optimization under nonlinear constraints. [PDF]

Math Program
Muehlebach M, Jordan MI.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
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

Layer-Specific Astrocyte Morphological Responses in the CA3 Hippocampus Region During Piry Virus-Induced Encephalitis. [PDF]

Hippocampus
de Almeida Miranda D, de Sousa AA, Dos Reis RR, Dos Santos ZA, Diniz JAP, da Costa Vasconcelos PF, Diniz CG, Anthony DC, Brites D, Diniz CWP, Diniz DG.   +10 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
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

Imperfect Synthetic Controls. [PDF]

J Appl Econ (Chichester Engl)
Powell D.
europepmc   +1 more source