Results 81 to 90 of about 23,345 (263)
Theory and Decision, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Advanced Intelligent Discovery, EarlyView.We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob +2 more
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Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
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Wild Guesses and Mild Guesses in Active Concept Learning
CoRRHuman concept learning is typically active: learners choose which instances to query or test in order to reduce uncertainty about an underlying rule or category. Active concept learning must balance informativeness of queries against the stability of the learner that generates and scores hypotheses.
Anirudh Chari, Neil Pattanaik
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Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks
Advanced Intelligent Discovery, EarlyView.Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç +2 more
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Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
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Circulant Digraphs with Larger Linear Guessing Number and Smaller Degree
MathematicsThe guessing number of a digraph is a new invariant in graph theory raised by S. Riis in 2006 and based on its applications in network coding and boolean circuit complexity theory. In this paper, we present the lower and upper bounds on a guessing number
Aixian Zhang, Keqin Feng
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Authentication With a Guessing Adversary
CoRR, 2015 6 pages, 1 figure, revised IEEE WIFS 2015 ...
Farshad Naghibi +2 more
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Advanced Intelligent Systems, EarlyView.A hybrid mobile robot with a modular Variable‐Stiffness Bridge transitions between a rigid locomotion platform and a flexible, shape‐conforming body. By enclosing objects within its deformable structure rather than relying on dedicated end effectors, the robot achieves orientation‐regulated planar transport, with conformal contact quality shown to ...
Luiza Labazanova +5 more
wiley +1 more source
Angewandte Chemie, EarlyView.π‐Extended COUPY dyes, obtained by vinylogation of coumarin‐based COUPY scaffolds, shift absorption, and emission deep into the NIR region while preserving compactness and synthetic accessibility. These bright, photostable dyes enable live‐cell imaging, FLIM, and site‐specific peptide conjugation, offering a modular platform for targeted bioimaging and
Diego Abad‐Montero +11 more
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