Results 221 to 230 of about 2,865,465 (299)

A Dual‐Branch Flux‐Based Extended Memristor Model With Machine‐Learning‐Assisted Calibration

Advanced Electronic Materials, EarlyView.
Multilayer oxide memristors integrated in crossbar arrays are described through a dual‐branch, flux‐controlled compact model. A three‐stage calibration workflow combining Latin hypercube sampling, Bayesian optimization, and gradient‐based refinement extracts device parameters from experimental data.
Davide Rossetti, K. Nikiruy, A.S. Demirkol, Alon Ascoli, M. Ziegler, R. Tetzlaff, Fernando Corinto   +6 more
wiley   +1 more source

Cross-domain zero-shot semantic segmentation for unstructured environments via EVA-CLIP model, ensemble prompt engineering, and optimized text-image matching. [PDF]

PLoS One
Zhou N, Zhao X, Zhou F, Li J, Yue X.
europepmc   +1 more source

AI‐Assisted Ultrasensitive Biosensing using Metal‐Electrolyte‐Metal‐Insulator‐Silicon Structure Based on Oxygen‐Tunable Iridium Oxide Nanonets for Lysyl‐Oxidase‐Like‐2 Breast Cancer Detection

Advanced Electronic Materials, EarlyView.
An oxygen‐controlled iridium‐oxide (IrOx) nano‐net structure is developed to enhance the sensitivity of metal‐electrolyte‐metal‐insulator‐silicon (MEMIS) biosensors. Integrated with a deep learning model, this platform achieves a high accuracy of 94.8% for detecting the breast cancer biomarker LOXL2.
Chiao‐Fan Chiu, Pankaj Kumar, Long Nguyen Minh Le, Ping‐Hsuan Wu, Punnatorn Boonyoung, Shen Tingyang, Abhijit Aich, Yi‐Pin Chen, Shih‐Yin Huang, Siddheswar Maikap   +9 more
wiley   +1 more source

Unveiling the interactions between design optimization, modeling strategies, and spatial analysis in genomic selection. [PDF]

Theor Appl Genet
Fernández-González J, Comadran Trabal J, Haquin B, Allard A, Combes E, Bernard K, Isidro Y Sánchez J.   +6 more
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference. [PDF]

Stat Med
Shen AA, Ben-Michael E, Feller A, Keele L, Murray J.   +4 more
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Optimizing resource allocation in <i>Miscanthus</i> breeding via sparse testing designs for genomic prediction. [PDF]

Front Plant Sci
Proma S, Lubanga N, Sacks E, Leakey ADB, Zhao H, Ghimire BK, Lipka AE, Njuguna JN, Yu CY, Seong ES, Yoo JH, Nagano H, Anzoua KG, Yamada T, Chebukin P, Jin X, Clark LV, Petersen KK, Peng J, Sabitov A, Dzyubenko E, Dzyubenko N, Glowacka K, Nascimento M, Campana Nascimento AC, Dwiyanti MS, Bagment L, Shaik A, Garcia-Abadillo J, Jarquin D.   +29 more
europepmc   +1 more source

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

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme. [PDF]

Stat Methods Med Res
Lovblom LE, Briollais L, Perkins BA, Tomlinson G.   +3 more
europepmc   +1 more source