Results 151 to 160 of about 4,772,239 (326)

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

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model

npj Computational Materials
We introduce a deep Kernel learning variational autoencoder (DKL-VAE) framework that integrates the generative power of a variational autoencoder (VAE) with the predictive nature of deep Kernel learning (DKL).
Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin   +4 more
doaj   +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

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

Identification of microbe–disease signed associations via multi-scale variational graph autoencoder based on signed message propagation

BMC Biology
Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health.
Huan Zhu, Hongxia Hao, Liang Yu
semanticscholar   +1 more source

Farmers' Preferences for Fossil‐Free Mineral Nitrogen Fertilizers: Role of Prices, Climate, Production Origin, and Identity

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Disruptions in global fertilizer markets and climate targets have increased interest in fossil‐free mineral nitrogen fertilizers. This study examines farmers' preferences for conventional (fossil‐based) and fossil‐free fertilizers using a labeled discrete choice experiment with 211 Swedish farmers.
Nasir Adam, Hui Tao, Gordana Manevska‐Tasevska, Per‐Anders Hansson, Vivian Wei Huang, Helena Hansson   +5 more
wiley   +1 more source

VAE-Var: Variational-Autoencoder-Enhanced Variational Assimilation [PDF]

Data assimilation refers to a set of algorithms designed to compute the optimal estimate of a system's state by refining the prior prediction (known as background states) using observed data.
Bai, Lei, Xue, Wei, Jia, Qilong, Xiao, Yi   +3 more
core   +1 more source

Shortcut Detection With Variational Autoencoders

2024 International Joint Conference on Neural Networks (IJCNN)
For real-world applications of machine learning (ML), it is essential that models make predictions based on well-generalizing features rather than spurious correlations in the data. The identification of such spurious correlations, also known as shortcuts, is a challenging problem and has so far been scarcely addressed. In this work, we present a novel
Nicolas M. Müller, Simon Roschmann, Shahbaz Khan 0009, Philip Sperl, Konstantin Böttinger   +4 more
openaire   +4 more sources

DDoS and DoS Mitigation Using a Variational Autoencoder

, 2019
DDoS and DoS attacks have been growing in size and number over the last decade. Existing solutions employed to mitigate these attacks have proven to be inefficient in combating the problem.
Bårli, Eirik Molde
core  

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source