Results 51 to 60 of about 234,090 (282)
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
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Frontiers in Earth ScienceIdentifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process.
Saâd Soulaimani +6 more
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Time-varying probability model of the reduction in bending capacity of RC beams due to corrosion of steel bars [PDF]
Archives of Civil EngineeringDue to the reduction in bending capacity of RC beams being affected by multiple stochastic uncertainties, employing a deterministic function model to study the bending capacity of RC beams often leads to analysis errors that are difficult to accept. This
Peng Tan, Shibin Kang, Zhanqiang Feng
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Artificial Intelligence as the Next Visionary in Liquid Crystal Research
Advanced Functional Materials, EarlyView.The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam +2 more
wiley +1 more source
International Journal of Distributed Sensor Networks, 2016 This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model.
Pao-Kuan Wu, Tsung-Chih Hsiao, Ming Xiao
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Phase Transitions of Neural Networks
, 1997 The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network.
Biehl M. +3 more
core +1 more source
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
IEEE AccessAiming at the limitations of existing agricultural pest image recognition technology, a novel agricultural pest recognition algorithm based on convolutional neural network and Bayesian method is proposed. During the process, convolutional neural networks
Ling Zhang, Fahui Wu, Wensen Yu
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Bayesian Recurrent Neural Networks
, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Fortunato, Meire +2 more
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Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics
Advanced Materials, EarlyView.An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang +8 more
wiley +1 more source