Results 141 to 150 of about 332,624 (268)

Accelerated Discovery of Topological Conductors for Nanoscale Interconnects

Advanced Science, EarlyView.
Copper interconnects exhibit a sharp increase in resistivity at ultra‐scaled dimensions, threatening continued miniaturization of integrated circuits. The gapless surface states of topological semimetals provide conduction channels resistant to localization.
Alexander C. Tyner, William Rogers, Po‐Hsin Shih, Yi‐Hsin Tu, Gengchiau Liang, Hsin Lin, Ching‐Tzu Chen, James M. Rondinelli   +7 more
wiley   +1 more source

Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning

Advanced Science, EarlyView.
We present a conformable wearable ultrasound patch for noninvasive bladder volume monitoring. A flexible PZT array combined with a concave acoustic lens concentrates acoustic energy and improves depth selectivity through the anterior pelvic wall.
Long Long Cao, Feng Wen Wang, Jingwei Xue, Fulei Liu, Ming Liang Jin   +4 more
wiley   +1 more source

Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption

Advanced Science, EarlyView.
Inspired by the regulation mechanism of window blinds, this study designs an electromagnetic wave‐absorbing metamaterial. By introducing the magneto‐electric coupling concept and integrating it with an artificial intelligence‐based data‐driven collaborative optimization strategy, the material optimizes impedance matching performance and enhances loss ...
Zhe Wang, Wanchong Li, Lu Feng, Zaiqing Yang, Shicheng Jin, Dongxu Zhao, Xiaoyong Wu, Yan Wang, Yu Mao, Jinsong Zhang   +9 more
wiley   +1 more source

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems

Advanced Science, EarlyView.
UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou, Chengchun Liu, Dongxiao Zhang, Yuntian Chen, Fanyang Mo   +4 more
wiley   +1 more source

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning

Advanced Science, EarlyView.
We present a new deep learning framework that hierarchically links molecular and functional unit attributions to predict electrolyte conductivity. By integrating molecular composition, ratios, and physicochemical descriptors, it achieves accurate, interpretable predictions and large‐scale virtual screening, offering chemically meaningful insights for ...
Xiangwen Wang, Muyang Chen, Gengyi Bao, Yan Lai, Jinghe Cao, Xinhua Liu, Rui Tan   +6 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

On the Mathematical Relationship Between RMSE and NSE Across Evaluation Scenarios

Model evaluation metrics play a crucial role in hydrology, where accurate prediction of continuous variables such as streamflow and rainfall–runoff is essential for sustainable water resources management and climate resilience. Among these metrics, the Nash–Sutcliffe efficiency (NSE) is the most widely adopted, while the Root Mean Squared Error (RMSE ...
openaire   +1 more source

Redefining the Health Risk of Battery Materials Through a Biologically Transformed Metal Mixture

Advanced Science, EarlyView.
Inhaled NCM particles undergo lysosomal degradation, releasing complex ion mixtures that induce systemic impact. The impact is determined by a critical balance between antagonistic Ni‐Co interactions and synergistic Mn effects. To capture these complexities in risk assessment, we develop an IAI model, ensuring a more accurate quantitative risk ...
Ze Zhang, Gan Miao, Xueyu Zhang, Zhao Shu, Dawei Lu, Yujie Song, Shanfa Yu, Qian Liu, Yang Song, Rong Zhang, Xiaoting Jin, Yuxin Zheng   +11 more
wiley   +1 more source

Out-of-distribution evaluation of active learning pipelines for molecular property prediction. [PDF]

RSC Adv
Yin T, Gao P, Panapitiya G, Saldanha EG.
europepmc   +1 more source

Machine‐Learning‐Guided Design of Incommensurate Antiferroelectrics via Field‐Driven Phase Engineering

Advanced Science, EarlyView.
The key to enhancing the energy storage performance of antiferroelectrics lies in regulating the phase transition and reverse phase transition. A phase‐field‐machine learning framework is employed to predict the energy storage performance of Pb‐based incommensurate antiferroelectrics with multi‐scale regulation strategy, thereby revealing the dynamic ...
Ke Xu, Xiaoming Shi, Zhaochen Xi, Shouzhe Dong, Changqing Guo, Rongzhen Gao, Letao Yang, Jing Wang, Di Zhou, Houbing Huang   +9 more
wiley   +1 more source