Results 141 to 150 of about 58,336 (216)

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

AOO-ANFIS: A Novel Bio-Inspired Neuro-Fuzzy Controller for Hydrostatic Transmission Stroke Control. [PDF]

Biomimetics (Basel)
Ülker H.
europepmc   +1 more source

Oxygen‐Scavenging‐Driven Interlayer Engineering for Balancing Linearity and Retention in IGZO Synaptic Memristors

Advanced Electronic Materials, EarlyView.
Oxygen‐scavenging‐induced self‐formed interlayers regulate Schottky‐barrier modulation in IGZO memristors, producing a fundamental trade‐off between synaptic update linearity and retention. A preserved AlOx interlayer moderates voltage distribution and enables gradual, nearly linear conductance updates, whereas a soft‐broken SiO2 interlayer provides a ...
Jae Woo Lee, Dong Hyeop Shin, Seung Joo Myoung, Soohong Eo, Wonjung Kim, Seeun Lee, Changwook Kim, Junjong Lee, Yoon Jung Lee, Sung‐Jin Choi, Sungjun Kim, Dae Hwan Kim   +11 more
wiley   +1 more source

Characterizing Biopolymer Electrolytes in Zinc–Air Batteries: Challenges, Best Practices, and a Robust Workflow Guiding Future Research Paths

Advanced Energy Materials, EarlyView.
Bio‐based gel polymer electrolytes promise sustainable, mechanically adaptable zinc–air batteries, yet their progress is constrained by inconsistent characterization. This review critically links formulation, structure, interfaces, and cell performance, identifies methodological gaps under alkaline operating conditions, and proposes application ...
Matteo Milanesi, Hiba Ali, Barbara Belza, Mattia F. Palermo, Claudio Gerbaldi, Giuseppe Antonio Elia, Nagore Ortiz‐Vitoriano   +6 more
wiley   +1 more source

Hybrid GA-DQL approach for efficient task mapping of IoT applications in fog computing framework. [PDF]

Sci Rep
Tripathy N, Sahoo S, Alghamdi NS, Dhiman G.   +3 more
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Optimized image preprocessing strategies for enhanced neural network-based defect detection in industrial automation. [PDF]

Sci Rep
Challa SP, de Leon MAL, Koziorek J, Hameed IA, Machacek Z.   +4 more
europepmc   +1 more source

Harnessing Digital Microstructure for Simulation‐Guided Optimization of Permanent Magnets

Advanced Intelligent Discovery, EarlyView.
An experimental‐to‐computational workflow is presented that transforms experimental 3D focused ion beam‐scanning electron microscopy data into a simulation‐ready digital microstructure for multiphase functional materials. Using heavy‐rare‐earth‐free Nd–Fe–B magnets as a model system, the approach quantifies grain connectivity across complex secondary ...
Nikita Kulesh, Anton Bolyachkin, Hajime Nakamura, Tadakatsu Ohkubo, Hossein Sepehri‐Amin   +4 more
wiley   +1 more source

Early prediction of gestational diabetes mellitus using deep learning applied to routinely collected clinical data. [PDF]

Front Endocrinol (Lausanne)
Cubillos G, Monckeberg M, Illanes SE, Perez CA.   +3 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source