ArticleACS omega2026
Battery Electrode Design Process Using Deep Neural Network.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The transition to electric vehicles (EVs) demands lithium-ion batteries (LIBs) customized for diverse energy and power density requirements, yet the intricate trade-offs among electrode design parameters make conventional trial-and-error optimization costly and time-consuming. This study presents the battery electrode exploration network (BattleNet), a deep learning model that predicts the specific capacity across multiple C-rates, collectively representing the rate capability, from electrode design parameters, including loading level, thickness, and porosity. Trained entirely on real-world experimental data, BattleNet achieved an
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Registered trials
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