Results 191 to 200 of about 11,467,392 (329)

Unified Phase‐Field Framework for Antiferroelectric, Ferroelectric and Dielectric Phases: Application to HZO Thin Films

Advanced Functional Materials, EarlyView.
HfxZr1−xO2${\rm Hf}_x{\rm Zr}_{1-x}{\rm O}_2$ offers CMOS‐compatible nanoscale ferroelectricity yet suffers from a high Ec${\rm E}_c$ demanding large operating voltages. A unified phase‐field framework spanning AFE/FE/DE phases shows how FE grains soften neighboring AFE grains over λ$\lambda$ ≈$\approx$ 22–37 nm.
P. Pankaj, Sandeep Sugathan, Jiyoung Kim, Si Joon Kim, Pil‐Ryung Cha   +4 more
wiley   +1 more source

Mutation-Aware Machine Learning Framework for Predicting Binding Affinity of Nirmatrelvir Analogs Targeting Coronavirus Main Proteases. [PDF]

Molecules
Rahman MS, Hasan MM, M Islam S.
europepmc   +1 more source

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Discovery of novel acridine based inhibitors of Bruton's tyrosine kinase (BTK) via pharmacophore modeling and machine learning-driven virtual screening followed by molecular dynamic studies. [PDF]

J Comput Aided Mol Des
Shahin R, Jaafreh S, Mansi I, Al Zoubi R, Abu-Irmaileh B, Azzam Y, Azzam S.   +6 more
europepmc   +1 more source

Recent progress of machine learning in flow modeling and active flow control

Chinese Journal of Aeronautics, 2021
Yunfei Li, Juntao Chang, C. Kong, W. Bao
semanticscholar   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Artificial intelligence-driven magnetic property prediction and materials discovery for next-generation spintronics. [PDF]

RSC Adv
Elilarassi R, Sivanandakumar D, Nagarajan P, Ilanchezhiyan P, Venkatachalapathy VSK, Velmurugan K.   +5 more
europepmc   +1 more source

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

, 2019
In this paper, we introduce a learning algorithm, boosted kernel ridge regression (BKRR), that combines L2-Boosting with the kernel ridge regression (KRR). We analyze the learning performance of this algorithm in the framework of learning theory. We show
Zhou, Ding-Xuan, Lin, Shao-Bo, Lei, Yunwen   +2 more
core  

Advances in Solution‐Processed Textile Triboelectric Nanogenerators: Ink Formation, Processing Strategies, Applications, and Challenges

Advanced Functional Materials, EarlyView.
Advanced ink systems for solution‐processed textile triboelectric nanogenerators are systematically summarized, spanning conductive, tribo‐negative, and tribo‐positive layers. By connecting ink chemistry, deposition methods, and device function, the present review reveals the key governing principles of solution development and highlights practical ...
Xinlong Sun, Stephen Beeby
wiley   +1 more source

Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts. [PDF]

Adv Mater
He Y, Cheng H, Zhou T, Sun T, Liu Q, He T.   +5 more
europepmc   +1 more source