NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
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Computer Science > Machine Learning
arXiv:2610.10776 (cs)
[Submitted on 7 Oct 2026]
Title:NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
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Abstract:Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost. These models describe local atomic environments accurately, but finite spatial cutoffs truncate long-range information flow, and stacking message-passing layers can lead to over-smoothing and over-squashing. Existing long-range extensions either prescribe a fixed analytical propagation kernel, restrict long-range communication to scalars or degree-preserving channels, are only approximately equivariant, or incur super-linear computational cost. Combining learnable long-range equivariant transport with multiscale many-body expressivity and efficient scaling for larger systems remains a central challenge. We introduce Neural Equivariant Multipole Operators (NEMORA), a neural equivariant extension of the Fast Multipole Method (FMM) for learning long-range tensorial representations. NEMORA generalizes the FMM's analytical multipole expansion and translation operators to learned equivariant counterparts on an adaptive spatial hierarchy. Its operators couple angular degrees and form many-body interactions across length scales, retaining the FMM's hierarchical organization and analytical radial factors as physical inductive biases while learning data-dependent long-range couplings. NEMORA evaluates in linear time and memory complexity, allowing it to treat larger systems than other long-range methods reaching hundreds of thousands of atoms, and it augments both symmetry-constrained and unconstrained short-range backbones. On non-local benchmarks, it reduces force and energy errors relative to the short-range backbones by over an order of magnitude and up to three orders of magnitude, respectively, which is better than or competitive with existing long-range extensions in accuracy.
| Comments: | 59 pages, 5 figures, including supplementary material |
| Subjects: | Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2610.10776 [cs.LG] |
| (or arXiv:2610.10776v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10776
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning, by Jay L. Kaplan and 3 other authors
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