Results 211 to 220 of about 842,734 (293)

Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity

open access: yesAdvanced Science, EarlyView.
Existing machine learning potentials remain limited in their ability to generalize across the broad chemical and structural variability of C–S–H. Here, a representative DFT‐labeled dataset is constructed through large‐scale model generation, redundancy‐controlled screening, first‐principles optimization, and targeted sampling of non‐equilibrium ...
Yunjian Li   +4 more
wiley   +1 more source

Generative AI‐Enabled Discovery and Experimental Demonstration of Ultra‐Broadband Epsilon‐Near‐Zero Perfect Absorbers

open access: yesAdvanced Science, EarlyView.
Researchers developed and applied a generative artificial intelligence (AI) model to optimize epsilon‐near‐zero thin film stacks for ultra‐broadband perfect absorption of light. The fabricated AI‐designed absorber, with a total thickness of only 160 nm, experimentally demonstrated an absorption bandwidth exceeding 1000 nm while maintaining greater than
David Dang   +10 more
wiley   +1 more source

Galactokinase 1 Positively Regulates Mitochondrial Respiration by Phosphorylating TIMM13 as a Protein Kinase

open access: yesAdvanced Science, EarlyView.
GALK1 acts as a protein kinase beyond the phosphorylation of galactose. GALK1 phosphorylates TIMM13 at Y73 in the cytoplasm. This phosphorylation prevents premature oxidative folding of TIMM13 and ensures its entrance into the intermembrane space of mitochondrion, positively regulating mitochondrial respiration.
Chang Woo Ko   +5 more
wiley   +1 more source

PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction

open access: yesAdvanced Science, EarlyView.
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley   +1 more source

LLMs as Representation Generators for Gradient‐Free Scientific Optimization: A Case Study in Battery Fast Charging

open access: yesAdvanced Science, EarlyView.
Large language models serve as representation generators for gradient‐free scientific optimization. Two complementary approaches produce executable battery‐charging policies either directly or through LLM‐designed neural representations coupled to Bayesian optimization.
Ge Lei   +3 more
wiley   +1 more source

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