Results 151 to 160 of about 7,096 (257)

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

open access: yesAdvanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi   +4 more
wiley   +1 more source

Large Language Model‐Informed Dual‐Track AI Framework for the Synergistic Design of Crack‐Free and High‐Strength Superalloys

open access: yesAdvanced Science, EarlyView.
This paper illustrates a knowledge‐augmented dual‐track AI framework for advanced superalloy design. First, Large Language Models translate metallurgical heuristics into explicit rules to rapidly prune a vast compositional search space. Subsequently, LLM‐distilled priors safely guide a reinforcement learning agent during autonomous process optimization,
Jian Yao   +9 more
wiley   +1 more source

Real‐Time Stress Visualization of Hydrogels Enabled by Supramolecularly Switched Stretch‐Induced Phase Separation

open access: yesAdvanced Science, EarlyView.
Supramolecular hydrogels enable real‐time visualization of mechanical stress through supramolecular switching mechanotransduction. Adamantane‐modified cellulose functions as a responsive domain that undergoes an on–off transition between hydrated and dehydrated states via reversible β‐cyclodextrin–adamantane complexation and decomplexation.
Sooyeon Noh   +7 more
wiley   +1 more source

Machine Learning Inversion Method for Elastoplastic Constitutive Parameters of Encapsulation Materials. [PDF]

open access: yesNanomaterials (Basel)
Gao M   +8 more
europepmc   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

open access: yesAdvanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio   +6 more
wiley   +1 more source

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

open access: yesAdvanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning   +3 more
wiley   +1 more source

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