Results 231 to 240 of about 19,092 (306)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Characterization of Off-Gases from an Inert Electrode Aluminum Electrolysis Cell. [PDF]

J Sustain Metall
Senanu S, Gunnarsson G, Gunnarsson D, Kjos O, Gaertner H, Meirbekova R, Magnusson JH.   +6 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Compact High-<i>Q</i> Bandpass Filter Using 3-D Stacked Stripline. [PDF]

Micromachines (Basel)
Cao Y, Liu Y, He J, Xu X.
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Advanced preclinical testing of a design-optimized ceramic bearing for a cervical total disc replacement. [PDF]

PLoS One
Kölle L, Pryce G, Beadling AR, Bryant M, Hall RM, Ferguson SJ, Helgason B.   +6 more
europepmc   +1 more source

Versatile Standing Wave Generation Between Arbitrarily Oriented Surfaces Using Acoustic Metasurface Deflectors and Retroreflectors

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel method for generating versatile standing wave fields using an acoustic metasurface deflector and retroreflector is introduced. By overcoming traditional constraints of parallel surfaces, the approach enables customizable wave patterns and enhances applications in particle manipulation.
Chadi Ellouzi, Farhood Aghdasi, Ali Zabihi, Amir K. Miri, Chen Shen   +4 more
wiley   +1 more source

OpenMCT: an open-source DC motor control educational kit. [PDF]

HardwareX
Von Chong A, Vargas S, Duhé JF, Cárdenas D.   +3 more
europepmc   +1 more source

Ultrasonic Rainbow: A Meta‐Transducer for Frequency‐Selective Elastic Wave Control via Dithering‐Based Wavenumber Tuning

Advanced Intelligent Systems, EarlyView.
A piezoelectric meta‐transducer generates an ultrasonic rainbow, steering guided elastic waves to different angles according to frequency. A dithering‐based binary electrode design enables a closer realization of the target wavenumber filter, improving directional purity and suppressing unwanted lobes. Numerical and experimental validation demonstrates
Masoud Mohammadgholiha, Jochen Moll, Kilian Tschöke, Carsten Kruska, Sylvia E. Gebhardt, Luca De Marchi   +5 more
wiley   +1 more source

Upcycling waste iron into high-performance Fe₃Si-SiC-NbC in-situ nanocomposites enhances multiple properties during carbothermal reactions. [PDF]

Sci Rep
Taha MA, Gad SA, Ngida REA, Youness RA.
europepmc   +1 more source