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    AI for science· 2017

    SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

    Kristof T. Schütt (first author) · 2017

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    arXiv 1706.08566OpenAlex W2726670313

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    Why it matters

    Established continuous-filter convolutions for molecular energies, forces, and atomistic representations.

    Condensed from the expert lists' notes.

    The record

    First released
    2017 (from arXiv 1706.08566)
    Citations
    1,559 citations, as counted by Semantic Scholar logoSemantic Scholar (non-commercial licence)
    Named by
    both expert lists
    Also relevant to
    Graph learning
    Score
    0.344 · computed rank 497 · consensus 0.73, must-read 0, citations percentile 0.357 (2010s · 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), and Semantic Scholar logoSemantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.