ArticleiScience2026
Reliable machine learning deployment across domains in electrochemical energy storage materials.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning accelerates the discovery of electrochemical energy-storage materials but can fail under domain shift. We audit a fixed pretrained latent representation for porous supercapacitor materials, spanning porous carbons, metal-organic frameworks (MOFs), simulated MOFs, and external carbon/covalent triazine framework (CTF) datasets. Deployment risk is governed by latent geometry and local representational support rather than domain labels alone. Nearest-neighbor target-property discrepancy increases in extrapolative regions, while sparse neighborhoods show higher epistemic uncertainty. Combining latent distance with uncertainty yields interpretable safety maps that flag supported, cautionary, and unsupported deployment regimes, providing a reliability-first safeguard for machine learning (ML)-guided materials screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.