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    AI for science· 2021

    E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

    Simon Batzner (first author) · 2021 · published 2022

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    DOI 10.1038/s41467-022-29939-5arXiv 2101.03164OpenAlex W4225405705

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    Why it matters

    Demonstrated highly data-efficient, symmetry-preserving neural interatomic potentials for molecular dynamics.

    Condensed from the expert lists' notes.

    The record

    First released
    2021 (from arXiv 2101.03164)
    Published
    2022
    Citations
    1,935 citations, as counted by OpenAlex (CC0)
    Named by
    the chatgpt list only
    Also relevant to
    Graph learning
    Score
    0.277 · computed rank 661 · consensus 0.29, must-read 0, citations percentile 0.543 (2020s · science), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.