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Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges
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inferredcites →https://doi.org/10.1016/j.commatsci.2016.12.004openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jpowsour.2014.01.054openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.neucom.2019.10.118openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.pnsc.2019.04.003openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.procs.2017.08.021openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.proeng.2011.11.2647openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.ress.2015.07.013openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rser.2015.12.249openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rser.2017.05.001openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rser.2019.03.036openalex-snapshot
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