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arXiv Machine Learning By Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo

NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

Read the original on arXiv Machine Learning →

The paper introduces NEMORA, a neural equivariant extension of the Fast Multipole Method designed to capture long‑range tensorial interactions in atomistic machine learning models. NEMORA replaces the FMM’s fixed multipole expansion with learnable equivariant operators that operate on an adaptive spatial hierarchy, enabling many‑body interactions across multiple length scales while preserving physical inductive biases. The method achieves linear time and memory complexity, allowing it to handle systems with hundreds of thousands of atoms and significantly reducing force and energy errors on non‑local benchmarks compared to short‑range backbones.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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