Results 181 to 190 of about 203,799 (303)

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

open access: yesAdvanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

Van der Waals Heterostructures for Next‐Generation Spintronics: Multiferroic‐Mediated Magnetoelectric Properties

open access: yesAdvanced Science, EarlyView.
As CMOS technology approaches fundamental energy limits, new paradigms for low‐power information processing are required. Magnetic systems are attractive for their spin transport, yet current‐induced magnetization control is energy inefficient. Multiferroic heterostructures enable energy‐efficient electric field manipulation of magnetism.
Donghyeon Lee   +3 more
wiley   +1 more source

An Interpretable, Data‐Driven, Hierarchical Multi‐Domain Fusion Framework for Classification and Motor Function Scoring in Chronic Ankle Instability

open access: yesAdvanced Science, EarlyView.
An AI‐enabled digital twin framework integrates wearable EMG sensing with hierarchical multi‐domain fusion to classify chronic ankle instability, distinguish clinically relevant subtypes, and generate continuous motor function scores. Clinically interpretable functional stratification and SHAP‐based biomarker analysis provide transparent decision ...
Tianle Jie   +12 more
wiley   +1 more source

Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions

open access: yesAdvanced Science, EarlyView.
A unified time‐series forecasting framework learns transferable battery capacity‐degradation patterns from 20 heterogeneous datasets spanning chemistries, formats, temperatures, and cycling conditions. A single model delivers competitive predictions on both known and previously unseen datasets, while physics‐guided representation learning improves ...
Joey Chan   +8 more
wiley   +1 more source

A Unified Hierarchical Multiscale Fusion Framework for Drug–Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery

open access: yesAdvanced Science, EarlyView.
A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu   +7 more
wiley   +1 more source

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