ArticleRSC advances2026
Deep learning-driven anomaly detection and feature discovery in Ce-rich (Ni-Fe-Co-Ce)O
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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Authors and funding
3 authors.
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Abstract
Developing high-performance oxygen evolution reaction (OER) catalysts is vital for energy conversion. However, extracting rare, exceptional materials from massive high-throughput experimental datasets remains challenging, as conventional machine learning models often misclassify these optimal data points as noise. Here, we propose a deep learning-driven anomaly detection framework that overcomes this limitation. By integrating atomic-level descriptors with convolutional neural networks (CNNs) for similarity stabilization analysis, our model employs an iterative data-cleaning mechanism to automatically isolate and evaluate high-performing outliers. We validated this approach on a high-throughput (Ni-Fe-Co-Ce)O
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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.