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Machine learning-assisted DFT-prediction of pristine and endohedral doped (O and Se) Ge12C12 and Si12C12 nanostructures as anode materials for lithium-ion batteries

Crossref DOI link: https://doi.org/10.1038/s41598-024-77150-x

Published Online: 2024-10-31

Update policy: https://doi.org/10.1007/springer_crossmark_policy

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