mlsanityData to 7 Oct 2026

Neural P3M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs

arXiv 2409.17622 ↗First posted 26 Sep 2024cs.LG cs.AIcited by 9

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Abstract (arXiv)

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

13 of 26 references.

References · 13

  1. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  2. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  3. 2020Directional Message Passing for Molecular Graphscited by 556
  4. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  5. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  6. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  7. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  8. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  9. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  10. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  11. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
  12. 2023Efficient Approximations of Complete Interatomic Potentials for Crystal Property Predictioncited by 37
1 more
  1. 2022Capturing long-range interaction with reciprocal space neural networkcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 3.

9 papers.

  1. 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?cited by 18
  2. 2025Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibrationcited by 5
  3. 2025Geometric Hyena Networks for Large-scale Equivariant Learningcited by 4
  4. 2025Hierarchical geometric deep learning enables scalable analysis of molecular dynamicscited by 1
  5. 2026Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blinkcited by 1
  6. 2025MCGM: Multi-stage Clustered Global Modeling for Long-range Interactions in Moleculescited by 0
  7. 2025Reciprocal Space Attention for Learning Long-Range Interactionscited by 0
  8. 2026Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervisioncited by 0
  9. 2026NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learningcited by 0