2026-07-07 08:23 |
[PUBDB-2026-02057]
Journal Article
Eren, E. O. ; Senokos, E. ; Horner, T. ; et al
CVD-grown tunable carbon films for high-performance sodium storage
Sodium-ion batteries are considered a promising and sustainable energy-storage technology, yet achieving competitive energy density requires electrode materials with high reversible capacity. Here, we introduce a chemical vapor deposition strategy that allows the growth of uniform, electronically continuous carbon coatings onto highly porous carbon substrates for use as high-capacity negative electrodes. [...]
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2026-07-06 13:55 |
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2026-07-06 12:26 |
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2026-07-06 10:21 |
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2026-07-06 10:08 |
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2026-07-06 08:53 |
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2026-07-03 17:05 |
[PUBDB-2026-02005]
Journal Article/Contribution to a conference proceedings/Contribution to a book
Paszynski, M. ; Barnard, A. S. ; Zhang, Y. J. ; et al
Comparison of Epistemic Uncertainty Quantification Methods for Out-of-Distribution Detection in Autoencoder–RNN Surrogate Model of Molecular-Continuum Flow Simulations
2026Computational Science – ICCS 2026 Workshops / Paszynski, Maciej (Editor) [https://orcid.org/0000-0001-7766-6052] ; Cham : Springer Nature Switzerland, 2026, Chapter 31 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-032-29917-8=978-3-032-29918-5 ; doi:10.1007/978-3-032-29918-5 26th International Conference, ICCS 2026 Hamburg, Germany, June 29 – July 1, 2026, ICCS, HamburgHamburg, Germany, 29 Jun 2026 - 1 Jul 20262026-06-292026-07-01
Lecture notes in computer science 16789, (2026) [https://doi.org/10.1007/978-3-032-29918-5_31]2026
Neural network surrogates have been shown to decrease computational costs of simulations, but often at the risk of unreliable predictions. This work integrates and evaluates multiple epistemic uncertainty quantification methods for a reproduced convolutional autoencoder–recurrent neural network surrogate architecture for molecular data in a coupled spatiotemporal molecular-continuum flow prediction. [...]
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2026-07-01 17:10 |
[PUBDB-2026-01969]
Contribution to a conference proceedings/Journal Article
Mkrtchyan, T. ; Chub, A. ; Green, C. ; et al
Bridging Grid and HPC Computing for Data-Intensive Science: Scaling dCache for Modern Workflows
The increasing integration of high-performance computing (HPC) resources into data-intensive scientific workflows places new demands on storage systems traditionally developed for grid and distributed computing environments. dCache, a mature, exascale storage system jointly developed by Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory (FNAL), and the Nordic e-Infrastructure Collaboration (NeIC), has evolved to support a broad range of scientific communities beyond its origins in High-Energy Physics, including astrophysics, Photon science, and AI training. [...]
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2026-07-01 09:14 |
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2026-06-30 09:23 |
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