mlsanityData to 7 Oct 2026

Molecule Graph Networks with Many-body Equivariant Interactions

arXiv 2406.13265 ↗First posted 19 Jun 2024cs.LG cond-mat.mtrl-scicited by 3

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Abstract (arXiv)

Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop Equivariant N-body Interaction Networks (ENINet) that explicitly integrates l = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and generalized the formulation to N-body interactions. Experiments indicate that integrating many-body equivariant representations enhances prediction accuracy across diverse scalar and tensorial quantum chemical properties.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 77 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

35 of 77 references.

References · 35

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  11. 2020Directional Message Passing for Molecular Graphscited by 556
  12. 2018Spherical CNNscited by 452
23 more
  1. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  2. 2021Pay Attention to MLPscited by 367
  3. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 348
  4. 2016Steerable CNNscited by 298
  5. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
  6. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  7. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  8. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  9. 2021Geometric and Physical Quantities Improve E(3) Equivariant Message Passingcited by 225
  10. 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
  11. 2017Learning Steerable Filters for Rotation Equivariant CNNscited by 193
  12. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  13. 2018Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Networkcited by 168
  14. 2016Discriminative Embeddings of Latent Variable Models for Structured Datacited by 130
  15. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  16. 2022Benchmarking Graphormer on Large-Scale Molecular Modeling Datasetscited by 63
  17. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  18. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 55
  19. 2020Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Propertiescited by 51
  20. 2019TeaNet: universal neural network interatomic potential inspired by iterative electronic relaxationscited by 16
  21. 2023Joint Group Invariant Functions on Data-Parameter Domain Induce Universal Neural Networkscited by 5
  22. 2024Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potentialcited by 3
  23. 2024A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Computationally-efficient Graph Modeling with Refined Graph Random Featurescited by 2
  2. 2025PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learningcited by 0
  3. 2026Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Predictioncited by 0